PET Molecular Imaging: A Holistic Review of Current Practice and Emerging Perspectives for Diagnosis, Therapeutic Evaluation and Prognosis in Clinical Oncology.

1. Introduction

Awarded the medical invention of the year by Time Magazine in 2000, PET/CT molecular imaging has been imposed in clinical oncological practice over the past 20 years, substantially modifying the management of many cancer subtypes in daily practice [ 1 , 2 , 3 ]. Supported by the International Atomic Energy Agency, the Lancet Oncology commission on medical imaging and nuclear medicine—an international consortium established in 2018 to inventory and promote access to imaging and nuclear medicine for cancer care—has emphasized the substantial health and cost benefits of scaling up access to imaging and nuclear medicine for cancer care worldwide [ 4 ]. In particular, PET/CT and theranostics are now recommended imaging modalities for cancer care in tertiary health care centers.

Based on the well-grounded foundations of quantification and radiolabeled molecular probe vectorization, the goal of this article is to provide a holistic overview of the capabilities, current practice and emerging perspective of PET-molecular imaging for diagnosis, therapeutic evaluation and prognosis in clinical oncology.

2. PET Molecular Imaging: A Powerful Quantitative Imaging Tool with Different Levels of Analysis

PET imaging was mainly developed in the 1970s for brain research purposes. In the early 2000s, PET/CT hybrid imaging was rapidly imposed worldwide as a critical oncological imaging tool, paving the way for the molecular imaging-based assessment of tumors in clinical practice. During the last 20 years, constant technical improvements have led to a 3- to 5-fold decrease in injected radioactivity in clinical practice, making the debate on radiation exposure clearly outdated in view of the major benefit obtained in daily patient care. The recent FDA-cleared and CE-marked deep learning solutions that provide dose-reduced AI-based FDG PET/CT should optimize patients’ radiation exposure even more in the future (SubtlePET TM AI [ 5 ]). With its very high detection sensitivity properties (picomolar, 10 6 higher than standard morphological imaging), PET is a powerful imaging tool that can be used to quantify various biological processes. By quantification, one must understand a simple linear relationship between the numerical pixel value N measured in a region of interest (ROI) of the image and the biological radiotracer concentration [ C ] in the related tissue structure: N = k [ C ] . Importantly, this fundamental property depends on the control of many technical and physical factors to make an optimized and accurate link between the patient and its related optimized PET imaging data [ 6 ]. Regular quality controls, standardized practical procedures and CT-based attenuation corrections are among the most important prerequisites. Additionally, one must be aware of the impact of image reconstruction parameters on the measured PET data. For these reasons, the European Association of Nuclear Medicine (EANM) launched the EANM Research GmbH (EARL) initiative in 2006 to promote the standardization of PET practices (clinical and research) at the international level [ 7 ]. In the same way, and because molecular imaging is impacted by the patient’s physiological condition, several drug intakes, fasting procedures and delay times between the radiotracer injection and the image acquisition must be verified. Therefore, numerous international guidelines have been published to standardize these procedures [ 8 , 9 , 10 ]. For example, considering FDG PET imaging, the most widely used PET radiotracer in clinical oncology, and because tumor glucose metabolism increases with time [ 11 ], a fixed 60-min delay time between radiotracer injection and PET acquisition has been defined [ 8 ]. Additionally, metabolism-modifying drugs such as G-CSF and physical activity must be avoided, and a 6-h fast before and euglycemia at the time of image acquisition must be verified [ 8 ]. Based on these important technical considerations, several levels of quantification have been historically developed for PET imaging: from visual interpretation to full kinetic modeling, the level of information provided to the clinician is not the same and does not require the same acquisition procedures ( Figure 1 ).

2.1. Visual Analysis

This first level of analysis only requires static PET imaging data (the standard of care in clinical practice): the radiotracer is injected intravenously into the patient, and a few minutes of PET acquisitions (typically 2 to 5 min per bed position, which is defined by the PET axial field of view) are started after a fixed delay time from the injection (e.g., 60 min for 18 F-FDG). The resulting PET imaging data are, thus, an average snapshot of the entire acquisition time for each PET bed position ( Figure 1 ). Visual analysis considers any non-physiological radiotracer uptake as abnormal. Although this protocol is very simple to use, accurate knowledge of radiotracer biodistributions is required ( Figure 2 ). Additionally, this pattern-based approach is subjective and remains purely qualitative. To reduce the inter- and intraindividual variabilities, visual grade normalizations to reference regions have been proposed (cf infra). The diagnostic and prognostic capabilities of PET visual analyses over conventional imaging are perfectly illustrated in FDG-avid lymphoma patients [ 12 ], with reported management changes up to 45% at initial staging [ 13 ] and improved outcome prediction (both progression and event-free survival) at both early and end-of-treatment evaluations (for extensive review, please refer to Appendices A1–A3 of [ 12 ]). In the same way, other well-grounded illustrative foundations emphasizing the high suitability of visual-based PET assessments for the diagnosis or management of oncological patients include malignant melanoma [ 14 ], non-lepidic non-small-cell lung cancer ( 18 F-FDG) [ 1 , 15 , 16 ], paraganglioma syndromes ( 18 F-FDOPA for SDHC, SDHD and SDHAF2 gene mutations) [ 17 , 18 , 19 ] or, more recently, multiple myeloma [ 20 ].

2.2. Semi-Quantitative Analyses

To measure the biological aggressiveness of tumors and to monitor the response to treatment from standard-of-care static PET imaging data with more objective metrics, semi-quantitative approaches have been increasingly used in clinical studies. The standardized uptake value (SUV), the most widely used semi-quantitative PET metric that was first used in the 1980s [ 21 ], is a unitless parameter defined by the ratio of the time-decay-corrected radioactivity concentration measured in a region of interest ( C t a r g e t , in MBq / mL ) to the radioactivity injected in the body ( C i n j e c t e d , in MBq), normalized by the body weight (in g) or equivalent: S U V b w = C t a r g e t C i n j e c t e d / W e i g h t × 1 g / mL . SUV normalization to body surface area (SUV bsa ) or, more recently, lean body mass (SUV lbm or SUL) instead of body weight, has been proposed, and SUL is the current preferred metric for response assessment studies because of its lower dependency on body weight changes [ 8 ]. From this general concept, many SUV-based metrics have been reported since the 1990s, including SUV max (the maximum pixel activity measured in the region of interest), SUV mean (the mean of pixel activities measured in the region of interest) or SUV peak (the mean of pixel activities measured in a volume of 1 cm 3 centered on the higher uptake part of the region of interest), but also target background SUV ratios (SUV r ) and composite whole-body scores, such as metabolic tumor volume (TV = ∑ 1 n ( V o l u m e n ) for n lesions identified at the whole-body level) or total lesion glycolysis (TLG = ∑ 1 n ( S U V m e a n n × V o l u m e n ) for n lesions identified at the whole-body level) ( Figure 3 ). Importantly, all these SUV-based metrics intrinsically depend on numerous physical and technical factors [ 22 , 23 ], strongly limiting the absolute comparability at the multicenter level except if using proper standardization procedures [ 7 ]. Additionally, the semiautomated segmentation procedures used to generate metabolic volumes of interest are still mainly based on simple fixed thresholds in clinical practice (typically 3D isocontour at 41% or 50% of the maximum pixel value) [ 8 ], despite their well-known inaccuracy to delineate heterogeneous tumor lesions. The segmentation task is an entire research topic in itself, illustrating the richness of the many possibilities developed in this field [ 24 ]. In general, SUV-based metrics and derivatives have shown their relevance for early response assessments of treatment or prognosis in various malignancies, such as gastric cancer [ 25 ], rectal cancer [ 26 ], lymphoma [ 27 , 28 , 29 , 30 ], gastrointestinal stromal tumors (GISTs) [ 31 , 32 ] or multiple myeloma [ 33 , 34 ].

2.3. Kinetic Modeling

In fact, visual and semi-quantitative PET metrics derived from static PET images provide limited information. Using dynamic PET acquisitions provides more sophisticated metrics, allowing the quantification of advanced metabolic pathways at the cellular level ( Figure 1 ). From Patlak simplified graphical analysis [ 35 ] to full compartmental analyses (1, 2, 3 or more compartments depending on the estimated biological function: perfusion, enzymatic activity, receptor binding) [ 36 ], PET kinetic modeling is considered the reference standard for absolute quantification. Compared to standard-of-care PET static measurements, dynamic PET metrics have provided promising results to improve the diagnosis, response assessment or prognosis of various malignancies [ 37 ]. To date, historical drawbacks have limited its use in clinical practice. In particular, the need for time-consuming acquisition schemes (up to one-hour continuous PET acquisition to estimate the full compartmental modeling of glucose metabolism [ 38 ]), the very limited axial coverage of the body extent in standard-of-care PET/CT devices and the complexity of image processing remain major limiting factors. However, the recent FDA clearances of large axial coverage and total body PET/CT systems [ 39 , 40 , 41 ], together with potential time-reduced acquisition procedures [ 42 , 43 ], improved reconstructions and postprocessing tools, will probably democratize dynamic whole-body PET in research and clinical practice, boosted by the growing molecular targeting requirements related to oncological drug development [ 44 ].

3. PET Molecular Imaging: Response Evaluation Criteria in Practice

The use of robust, easy-to-use and reliable interpretation criteria is of particular importance in oncological imaging. In clinical practice, many efforts have been made over the last two decades to standardize image analyses at an international level. Supported by historical RECIST morphological-based models, the response criteria are traditionally defined by four main categories: complete response (CR), partial response (PR), stable disease (SD) or progressive disease (PD). Although RECIST and its revised versions historically dominate this field in clinical trials [ 45 ], its limitations in predicting survival outcome or response to treatments and the massive deployment of PET imaging in worldwide oncological practice have motivated the emergence of PET-based response criteria in many cancer diseases. Depending of the targeted disease, several PET-based international criteria have been proposed as described in the next sections.

3.1. Solid Tumors

A general historic of response criteria for solid tumor is provided in Table 1 . In 1999, the European Organization for Research and Treatment of Cancer (EORTC) first introduced metabolic information from 18 F FDG-PET imaging (namely, SUV max ) into the response assessment criteria of oncological diseases [ 46 ]. In practice, the EORTC criteria remained in the background of RECIST until 2009 because of the well-known inherent inter-center variability of the SUV max PET metric [ 47 ]. In 2009, a very large step was made with the introduction of the PET-Response Criteria in Solid Tumors (PERCIST) [ 48 ]. For the first time, a critical effort has been made to propose a well-defined, reliable and robust standardized PET methodology in this field ( Figure 4 ). To overcome the limitations of SUV max , SUV peak was introduced, together with SUV normalization by lean body mass (SUL) and a precise explanation of the definition and number of measurable targeted tumor lesions. Additionally, the percent change in SUL peak between the two examinations was set to 30% to integrate the inter-center variabilities. The PERCIST criteria take full interest, particularly in the assessment of cytostatic chemotherapies, as they can demonstrate metabolic changes when no anatomical changes are observed. To date, PERCIST has surpassed RECIST in numerous cancer diseases for the prediction of patient outcome or the assessment of responses to treatment in many cancer diseases, emphasizing the relevance of this powerful PET-based evaluation tool. In particular, breast cancer [ 49 ], esophageal cancer [ 50 ], Ewing sarcoma [ 51 ] and non-small-cell lung cancer (NSCLC) [ 52 , 53 ] have been investigated for this purpose. With the emergence of immunotherapy, standard imaging criteria (both morphological and metabolic-based) have rapidly required refinements to integrate atypical response patterns such as pseudoprogression or hyperprogression diseases, which can lead to significant misclassification of patients [ 54 ]. To mirror the morphological definition of two new categories of response assessment to treatment in the dedicated iRECIST update (namely, unconfirmed and confirmed progression diseases), iPERCIST criteria were recently proposed and tested retrospectively in a study of 28 cases of NSCLC being treated with nivolumab [ 55 ]. Previously, PET/CT Criteria for Early Prediction of Response to Immune Checkpoint Inhibitor Therapy (PECRIT) have been assessed prospectively in advanced melanoma under immune checkpoint inhibitors [ 56 ]. Combining anatomical and functional imaging data to predict eventual response to ICI reached 100% sensitivity, 93% specificity and 95% accuracy. However, these findings are mainly limited by the small number of patients evaluated and require validation in larger cohorts. More recently, Anwar H. and coworkers proposed the PET Response Evaluation Criteria for Immunotherapy (PERCIMT) in 41 melanoma patients [ 57 , 58 ]. Interestingly, PERCIMT criteria consider the number of new lesions and their extent during therapy, which allows better patient stratification compared to standard SUV-based parameters. The new appearance of ≥4 metabolically active lesions with functional diameters <1.0 cm or ≥3 lesions >1.0 cm in diameter seemed correlated with real progression rather than pseudoprogression. At the same time, 18 F-FDG PET imaging offers a unique opportunity to detect immune-related side effects at the whole-body level, such as thyroiditis, gastritis, colitis, pneumonitis, sarcoidosis [ 59 ] and polymyalgia rheumatica-like syndromes [ 60 ]. Interestingly, few studies have reported an association between immune activation [ 61 ] or immune-related side effects assessed by PET and patient response [ 62 , 63 ]. Although these recent results must be confirmed in larger prospective studies, metabolic-based imaging appears to be a promising tool in this field.

3.2. Lymphoma

In Hodgkin Lymphoma (HL) and Non-Hodgkin Lymphoma (NHL, mainly Diffuse large B-cell lymphoma DLBCL), given the natural high avidity of aggressive tumor cells for 18 F-FDG and the binary metabolic response to treatment, dedicated PET response evaluation criteria have progressively emerged to currently become the most powerful imaging tool in this field ( Table 2 ). Considering the extensive evidence-based literature of the first decade of the 21st century, PET was integrated into the international guidelines for response assessment of lymphoma in 2007 [ 64 ], a revised version of the historical international working group morphological criteria [ 65 ]. Seven years later, the Lugano criteria refined these guidelines [ 66 ], confirming the role of PET as a key imaging modality in the diagnosis and response assessment of lymphoma. PET imaging was definitively integrated into the Ann Arbor staging system (Lugano staging classification), and the PET Deauville 5-point visual grading scale was defined as the main response evaluation tool (in standard therapeutic schemes, end-treatment scores of 1, 2 or 3 were considered CR, whereas scores of 4 and 5 were considered non-responses, as illustrated in Figure 5 ). Additionally, based on the relevance of PET in this field, bone marrow biopsy is no longer indicated for the assessment of bone marrow involvement in HL and is only required in negative PET cases with consequences for patient management in DLBCL. To increase the reliability of the procedure and improve the prognostic value of PET imaging, interim SUV-based semiquantitative metrics are currently being extensively investigated [ 28 , 67 , 68 ]. In particular, a very recent large prospective study with 158 new DLBCL patients reported higher prognostic value for interim SUV-based metrics (interim ΔSUV max = percent change between baseline PET and PET performed after two cycles of treatment) compared to the standard visual grading scale evaluation [ 69 ]. In particular, only the interim ΔSUV max predicted both PFS and OS. These findings and those of other recent clinical trials highlight the relevance of using more quantitative PET methods in this field [ 70 , 71 ]. Finally, for solid tumors, PET imaging criteria were recently adapted to immunotherapy by integrating indeterminate response features: the lymphoma response to immunomodulatory therapy criteria (LYRIC) in 2016 [ 72 ] and the Response Evaluation Criteria in Lymphoma (RECIL) in 2017, with more morphological parameter weighting [ 73 ].

3.3. Multiple Myeloma

Magnetic resonance imaging (MRI) is considered the gold standard to detect bone marrow involvement in multiple myeloma due to its very high anatomical resolution and soft tissue contrast. However, its limited value in assessing the response to treatment and the time-consuming whole-body acquisition protocols have promoted the use of 18 F-FDG PET/CT as a fast-whole-body complement imaging modality to manage patients with MGUS and multiple myeloma. In comparison with MRI, 18 F-FDG PET/CT shows faster normalization of image findings [ 74 ]. Recently, the International Myeloma Working Group has integrated 18 F-FDG PET/CT into the diagnostic criteria of multiple myeloma, considering abnormal bone 18 F-FDG uptake as a C.R.A.B feature (C = hypercalcemia; R = renal failure; A = anemia; and B = bone lesions) [ 75 ]. Although clinically promising in this field, several drawbacks make the interpretation of 18 F-FDG PET challenging in multiple myeloma, especially at the time of initial diagnosis: as a heterogeneous disease, multiple myeloma can present with variable metabolic uptake, ranging from low to extremely high; additionally, anemia and G-CSF may stimulate BM uptake, reducing the detectability of axial targets. All these challenging aspects and pitfalls require extensive experience and knowledge of the particular aspects of MM. In past years, different interpretation criteria have been proposed. Some groups have proposed semiquantitative SUV-based parameters to assess the disease burden [ 76 ]. Recently, a consortium of nuclear medicine experts, hematologists and medical physicists proposed standardized criteria to promote the use of PET in clinical trials: the Italian myeloma criteria for PET use (IMPeTUs). These new criteria, combining the Deauville five-point scale and morphological features (site and number of lytic lesions, Table 3 ) [ 77 ], have provided high reproducibility and can be considered a basis for harmonizing PET interpretation in multiple myeloma. Although recent findings showed the prognostic value of these criteria for both OS and DFS [ 20 ], further prospective clinical trials are warranted to confirm the relevance of IMPETUS in this field.

4. PET Molecular Imaging: The Promising Clinical Perspective of Radioligand Molecular Imaging and Therapy

PET imaging detection is conceptually based on the radionuclide labeling of molecular probes. Considering several intrinsic chemical limitations of vector-radioligand coupling, PET molecular imaging provides almost unlimited opportunities to map numerous physiological or pathophysiological targeted processes at the whole-body level, with picomolar detection sensitivity ( Figure 6 ). In the last decade, the integration of PET imaging with radionuclide therapy, also known as radiolabeled theranostics, has paved the way towards highly sensitive radionuclide-based precision medicine. In particular, major breakthroughs are currently expected in two clinical fields: neuroendocrine tumors (NETs) and prostate cancer (PCa).

4.1. NETs

According to the Surveillance Research Program of the National Cancer Institute [ 78 ], NETs account for only 0.49% of all malignancies. However, an unexplained increased incidence has been observed over the past decades. The emergence of somatostatin receptor-based PET diagnostic imaging revolutionized patient care in the late 1980s, for whom strategy management was very limited [ 79 , 80 ]. A further step was reached with the emergence of PET-based somatostatin receptor-imaging in the 2000s, combining the inherent higher physical properties of PET systems over conventional scintigraphy (spatial resolution, sensitivity detection, unbiased quantification), improved kinetics (faster clearance and tissue penetration with the last generations of targeted radioligands) and chemical properties (chelation-based, allowing fast and rapid vectorized radioligand switching from diagnosis with 68 Ga to therapy with 90 Y, 177 Lu or 225 Ac) [ 81 , 82 , 83 ]. Based on 2105 patients, the pooled diagnostic performance of SSR-PET from 22 studies provided a sensitivity and specificity of 93% (95% CI 91–94%) and 96% (95% CI 95–98%), respectively [ 84 ], surpassing conventional scintigraphy in this field [ 85 ]. Such detection performance led to the integration of SSR-PET into the international strategy of patient management, from initial staging to recurrence and palliative care for almost all NETs [ 86 , 87 ] ( Figure 7 ). Recently, the phase III NeuroEndocrine Tumors Therapy clinical trial (NETTER-1) validated the use of SSR-based radioligand therapy in well-differentiated metastatic NETs of the midgut [ 88 ]: 177 Lu-SSR showed a 79% reduction in risk of progression and an estimated PFS of 40 months compared to 8.4 months for high-dose octreotide therapy (79% lower risk of disease progression or death in the 177Lu-Dotatate group). For undifferentiated high-grade NETs, SSR-based radioligand therapy is still debated [ 89 , 90 ]. Some findings suggest that aggressive and higher grade 2-3 NETs but still well-differentiated with sufficient expression of somatostatin receptor to be visualized through prognostic SSR-PET could benefit from SSR-based radioligand therapy [ 91 ]. The ongoing NETTER-2 clinical trial (registered as NCT03972488 ) should answer this question. Finally, beyond SSR-PET, standard FDG PET may be used as a complementary tumor burden mapping in the patient management strategy due to its powerful independent prognostic value [ 92 , 93 ].

4.2. PCa

Prostate cancer is the second most frequent malignancy in men worldwide [ 94 ]. Because of the limited value of standard imaging, choline PET (a membrane phospholipid radiolabeled either with 11 C or 18 F) was widely used in the 2000s for the early assessment of biochemically recurrent prostate cancer. Extensive literature has shown choline PET to be of high relevance for the detection of recurrent lymph nodes or bone metastases in treated patients, especially with PSA above 2 ng/mL or high PSA velocity/doubling time [ 95 , 96 , 97 ]. However, poorer detection rates for PSA under 2 ng/mL and low specificity for treatment-naïve patients motivated the development of more specific molecular probes in this field. In this context, prostate-specific membrane antigen (PSMA), a transmembrane protein of unclear biological function that is overexpressed 100- to 10,000-fold in PCa cells compared to normal tissues [ 98 , 99 , 100 ], has gained increasing interest in the last decade. Given its high sensitivity for the detection of PCa lesions, PSMA radiolabeling is currently changing the methods for managing early biochemically recurrent PCa and occult metastatic PCa, for which conventional imaging modalities (MRI and CT) lack sensitivity and specificity [ 101 ]. Except for neuroendocrine PCa [ 102 ], PSMA expression increases with tumor dedifferentiation and in metastatic castration-resistant prostate cancer. A recent pooled analysis of 5113 patients (43 studies) imaged with 68 Ga PSMA PET showed overall detection rates of 94% at PSA levels above 2.0 ng/mL for biochemically recurrent PCa treated by radical prostatectomy, with higher detection rates than conventional imaging at PSA levels under 0.5 ng/mL [ 103 ]. Moreover, numerous studies have shown that PSMA PET radiotracers surpasses the actual concurrence (choline or fluciclovine PET radiotracers) to detect biochemically recurrent PCa, especially at PSA levels under 1 ng/mL (for reviews, please refer to [ 104 , 105 ]). Beyond recurrent disease management and given the growing literature in this field, especially the very good agreement with histology [ 106 , 107 ], PSMA PET has been rapidly imposed for the primary management of PCa patients [ 108 , 109 , 110 ]. In December 2020, the US Food and Drug administration granted the first clinical approval for institutional use (University of California, UCLA and USCF) of 68 Ga PSMA PET (PSMA-11) for the initial staging and the detection of recurrence in PCa patients based on recent clinical trial findings [ 111 , 112 , 113 , 114 ]. This milestone achievement should pave the way to a new standard of care for patients with PCa. From this perspective, and for simpler availability and production workflows, several diagnostic 18 F-radiolabeled PSMA PET tracers have already been integrated into the research pipeline [ 115 , 116 , 117 , 118 ]. Together with these recent imaging advances, PSMA radioligand therapy has also emerged in patients with metastatic castration-resistant PCa ( Figure 8 ). Mainly based on small-molecule inhibitors of PSMA (PSMA-I&T, PSMA-617), 10 large phase I-III PSMA RLT trials are currently ongoing and are using either 177 Lu or 225 Ac radionuclides (for details please refer to [ 119 ]). Among the promising literature findings already published, two prospective clinical trials are considered a very large step in this field: the LuPSMA trial, a single-arm phase 2 study that assessed the safety and efficacy of 177 Lu-PSMA-617 in 30 patients with metastatic castration-resistant PCa [ 120 ], and the TheraP multicenter randomized open-label phase 2 trial, which showed a decrease in PSA levels of at least 50% from baseline for the 177 Lu-PSMA-617 group compared to the cabazitaxel group, along with longer PFS (HR 0.63, p = 0.0028), lower toxicity events and higher pain improvement [ 121 ]. Finally, the very recent first historical standardized reporting guideline for PET imaging in this field emphasizes the growing importance of PSMA-based radiotracers in the management of PCa and the strong motivation of the international nuclear imaging community to accelerate its use in future clinical trials [ 122 ].

4.3. Other Near-Future Promising Perspectives in Clinical Practice: Immuno-PET and Beyond

Beyond the emerging success of NETs and PCa radioligand targeting, various molecular cancer targets have been developed since the historical use of 18 F-FDG in the late 1980s [ 123 ], based on the fundamental aerobic glycolytic properties of cancer cells, also known as the “Warburg effect” [ 124 ]. Emphasizing the unique capability of PET molecular imaging to constantly adapt to emerging oncological challenges, other biological properties of cancer have been assessed in numerous phase 0–3 trials, including hypoxia, apoptosis or protein synthesis (for review please refer to [ 125 ]). The hallmark capabilities of cancer are an evolving concept [ 126 ], and tumor immunity (tumor cells and the interlinks with their immune microenvironment) is currently a very hot topic of interest, given the revolution provided by immunotherapy checkpoint inhibitors [ 127 , 128 ]. Despite progressing results since the first FDA approval in 2011, the response rate to checkpoint inhibitors remains approximately 13% of eligible patients [ 129 ]. Many efforts are currently made to better identify patients who would be eligible for these new drugs [ 130 , 131 ]. Additionally, the numerous updates of limited current imaging criteria illustrate the needs of new tools to better characterize the immune ecosystem of tumors to improve treatment strategies [ 132 , 133 ]. Immune checkpoint-targeted radiolabeled monoclonal antibodies (IC-PET) and fibroblast activation protein-targeted radiolabeled inhibitors (FAPi-PET) are two serious candidates in this field. In the last five years, several dozen preclinical IC-PET studies with 64 Cu, 89 Zr, 68 Ga, 124 I or 18 F radionuclides have been published, but numerous challenging technical considerations remain to reach optimal IC-PET pharmacokinetics and biodistribution in practice (size, Fc-mediated functions and charge, radiolabeling strategy) ( Figure 9 ); for a review, please refer to [ 134 ]. Two very recent first-in-human studies assessed its clinical safety and feasibility [ 135 , 136 ]. The results from the study by Niemeijer and coworkers showed that for 13 patients with advanced NSCLC (follow-up of 3 months), IC-PET ( 89 Zr-nivolumab) findings correlated with immunohistochemistry [ 136 ]. In the study by Bensch F and coworkers, who included 25 patients with various cancer subtypes (bladder, NSCLC, triple negative breast cancers; median follow-up of 21.9 months), IC-PET ( 89 Zr-atezolizumab) better correlated with PFS and OS than immunohistochemistry or RNA-sequencing [ 135 ]. These very promising results have promoted the launch of numerous IC-PET clinical trials focusing on NSCLC, HNSCC, lymphomas, RCC, breast cancers and melanomas (for a review please refer to [ 134 ]). Finally, FAPi-PET has recently emerged as a PET molecule targeting cancer-associated fibroblasts (CAFs) [ 137 , 138 , 139 ]. CAFs play critical roles in tumor progression and immunity regulation and represent a promising therapeutic target [ 140 ]. Activated CAFs highly express FAP, a glycoprotein enzyme with peptidase activity [ 141 ]. Combining both PET molecular probes of tumor cells ( 18 F-FDG PET) and their surrounding stroma (FAPi PET) provides an open exciting perspective, as illustrated by recent findings in oncological patients with inconclusive 18 F-FDG PET findings [ 142 ], or to optimize the tumor volume delineation for radiotherapy planning [ 143 ]. Together with imaging capabilities, FAP-radioligand therapy is also under development [ 144 , 145 ], paving the way towards stroma-targeted radiolabeled theranostics.

5. PET Molecular Imaging: The Perspectives of Statistical and Computer Sciences for Multidimensional Image-Based Decision Making

In the last 20 years, the concept of tumor heterogeneity—where a tumor mass is considered a patchwork of distinct genotypic and phenotypic subcellular populations—has been imposed as a strong factor of treatment resistance [ 146 , 147 ]. In this context, the unsuitability of standard image-based criteria to accurately assess the response to nonconventional therapies [ 148 , 149 ], together with the rapid emergence of computer science in the field of medical imaging, promoted the development of radiomics—the extraction of high-throughput quantitative metrics from medical images—which is a large-scale image-based approach derived from OMICS (genomics, transcriptomics, proteomics, metabolomics, etc.) of which the purpose is to better capture tumor heterogeneity from standard-of-care medical images to build relevant diagnostic or predictive models [ 150 ]. Despite promising FDG PET results in the early 2000s [ 151 , 152 ] and efforts in recent years to propose “user-friendly” dedicated software for clinical use, radiomics has not yet reached a sufficient level of relevance in clinical practice [ 153 ]. In essence, the standard radiomics pipeline integrates image acquisition, segmentation and standardization, handcrafted feature extraction (intensity, shape and texture parameters) and selection through machine learning-based statistical analyses to build optimized diagnosis and prognosis endpoint models. One major drawback of such a huge operational processing pipeline remains its multilevel subjectivity and technical dependencies [ 154 ]. A recent systematic review based on 624 records (41 full-text articles mixing lung, head and neck, esophageal, rectal, cervical and breast cancers, mainly describing CT and PET findings) specifically addressed the repeatability and reliability of radiomic features and reported probable high variability at each main processing step for the majority of second- (shape-based metrics) and third-order (texture-based metrics) feature classes [ 155 ]. Nonetheless, this field of research is maturing based on years of hindsight. In 2020, the Imaging Biomarkers Standardization Initiative (IBSI), a task force of 20 research groups, established international standardized frameworks for radiomics computation and validated the use of 164 PET radiomics features [ 156 ]. Another major issue concerns the use of unsuitable statistical procedures in the vast majority of reported radiomics studies, probably partly explained by two factors: the strong difficulty of collecting and structuring large homogeneous datasets in “real-life” clinical practice [ 157 ] and the lack of knowledge in machine learning/data mining, a “new” field of skills to be learned by nuclear medicine practitioners. Again, the practice is maturing. Harmonization procedures have emerged to facilitate multicenter studies in practice and remove the so-called “center effect” [ 158 , 159 ]. Additionally, several statistical rules of thumb have gradually been imposed on the community, improving the development and validation of predictive models, particularly the use of a sufficient number of patients per radiomics feature, the dataset partitioning into independent training, validation and test subsets, the hyperparameter optimization and cross-validation of the algorithms and the use of objective performance metrics [ 160 , 161 , 162 ]. In the era of precision medicine, multiparametric imaging offers unique opportunities to characterize tumor behavior at an advanced multidimensional imaging level [ 163 , 164 ]. Interestingly, multiparametric radiomics has recently been shown to surpass single-modality procedures. In their proof-of-concept study, Vallières M. and coworkers demonstrated the superiority of joint FDG PET and MRI texture features to predict the risk of lung metastases in soft tissue sarcomas [ 165 ]: in a cohort of 51 biopsy-proven sarcomas, first- and second-order radiomics features were extracted from either FDG PET, MRI (T1- and T2-weighted) or fused PET/MRI baseline data. Among all the multivariable predictive models tested, the models constructed from fused PET/MRI data surpassed those generated either from PET or PET + MRI separated scans (AUC of 0.98 ± 0.002, sensitivity of 0.955 ± 0.006, specificity of 0.926 ± 0.004). More recently, Mu W. and coworkers compared the predictive value of single-modality and fused FDG PET/CT-based radiomic signatures (baseline scans) to predict the durable clinical benefit of immune checkpoint inhibitors in advanced NSCLC patients [ 166 ]. Trained on 99 patients and validated on two independent cohorts of 47 NSCLC (retrospective) and 48 NSCLC (prospective) patients, the predictive models, which included features generated from fused PET/CT data, were improved in all the training and test sets, reaching AUCs of 0.86 (95% CI 0.79–0.94), 0.83 (95% CI 0.71–0.94) and 0.81 (95% CI 0.68–0.92), respectively.

By capturing the pooled radiomic signatures of tumors across different imaging modalities, multiparametric radiomics changes the dimensionality of analyses: the extracted features no longer represent intervoxel relationships in a modality of interest but the multidimensional tumor behavior, driven by the properties of each modality. Such a paradigm shift will probably be enhanced with fully integrated PET/MRI systems, which have been clinically available since the beginning of the 2010s [ 167 ]. In this way, rather than using standard-of-care qualitative or semiquantitative PET, CT or MRI images, one could move towards more quantitative multiparametric analyses in future practice [ 168 ]. Moreover, fully automated and miniaturized production systems for PET radiotracer use in a “dose-on-demand” mode could stimulate multi-PET probe patient imaging in future clinical practice (iMiGiNE project, PMB-Alcen/SIGMAPHI/CEA).

To face the growing complexity of this arsenal of multidimensional data, the use of deep learning—a branch of machine learning using neural networks (convolutional neural networks (CNNs) in image processing)—appears to be a promising way to assist practitioners in the future. Because CNNs learn characteristics directly from raw images, feature extraction and prediction tasks can be performed jointly in an embedded process, bypassing classical handcrafted segmentation, feature selection or classification multilevel steps [ 169 , 170 ]. Moreover, two recent studies nicely illustrate the potential gain at the whole-body level of automated CNN-based detection and classification tasks in PET/CT. From 629 FDG PET/CT data (302 lung cancer and 327 lymphoma) retrospectively labeled by two nuclear medicine physicians, Sibille L. et al. trained, validated and tested a CNN research prototype (PET Assisted Reporting System PARS, Siemens Healthineers) to automatically detect and classify abnormalities (anatomic location, suspicious or nonsuspicious), reaching a classification AUC of 0.988 (95% CI 0.982–0.994) [ 171 ]. In their study, including almost 3500 FDG PET/CT data points from patients with various malignancies/benign diseases, Kawauchi K. et al. successfully classified PET data into three predefined classes (benign, malignant, equivocal), and their CNN reached accuracies of 99.4, 99.4 and 87.5% at the patient level [ 172 ]. Beyond evident improvements in the entire image processing workflow, numerous technical challenges will have to be faced before clinical validation. As an example, the recent external validation of the PET Assisted Reporting System (PARS, Siemens Healthineers) [ 171 ] on two French cohorts (cohort 1: 119 cases of DLBCL; cohort 2: 430 miscellaneous cancers) showed discrepancies between the reference manual and CNN-based procedures (cohort 1: median Dice score = 0.65; ICC TMTV = 0.68; cohort 2: median Dice score = 0.48; ICC TMTV = 0.61) [ 173 ]. For multidimensional image-based decision making, the level of image dimensionality (how many modalities? how many parameters? what level of quantification?) and the optimal way to integrate this multidimensionality in the depths of CNNs [ 174 ] will also require many research investigations. Regardless, deep learning-based multidimensional quantification can pave the way for more operational and holistic cancer molecular imaging analyses at the whole-body level ( Figure 10 ).

6. Conclusions

PET molecular imaging is a powerful imaging modality for quantifying tumor processes. After 20 years of clinical use around the world, PET/CT has proven its unique value for the diagnosis and therapeutic evaluation of many cancers. This maturity now allows us to consider a move towards more sophisticated levels of analyses in future clinical practice, boosted by the development of PET-based multimodal imaging, multiprobe radiolabeling and multidimensional data mining. A more holistic image-based representation of oncological processes becomes conceivable at the whole-body level, making PET molecular imaging a tool of choice in precision medicine for the next few years.

Acknowledgments

The authors would like to particularly thank Jérémie Calais, Martin Auerbach and Andrei Gafita (Ahmanson Translational Theranostics Division, UCLA, Los Angeles, CA, USA), Alizée Bouleau, Charles Truillet and Vincent Lebon (Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps) and Ludovic Le Meunier (Pmb-Alcen) for their help with pictorial support.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author Contributions

All authors (V.D., A.I., L.G. (Léa Gomez), L.G. (Lucas Goldfarb) and F.L.B.) have directly participated in the review process and in the writing of this article. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

1. van Tinteren H. Hoekstra O.S. Smit E.F. van den Bergh J.H. Schreurs A.J. Stallaert R.A. van Velthoven P.C. Comans E.F. Diepenhorst F.W. Verboom P. Effectiveness of positron emission tomography in the preoperative assessment of patients with suspected non-small-cell lung cancer: The PLUS multicentre randomised trial Lancet 2002 359 1388 1392 10.1016/S0140-6736(02)08352-6 11978336

2. Lindsay M.J. Siegel B.A. Tunis S.R. Hillner B.E. Shields A.F. Carey B.P. Coleman R.E. The National Oncologic PET Registry: Expanded medicare coverage for PET under coverage with evidence development Am. J. Roentgenol. 2007 188 1109 1113 10.2214/AJR.06.1175 17377055

3. Hillner B.E. Siegel B.A. Liu D. Shields A.F. Gareen I.F. Hanna L. Stine S.H. Coleman R.E. Impact of positron emission tomography/computed tomography and positron emission tomography (PET) alone on expected management of patients with cancer: Initial results from the National Oncologic PET Registry J. Clin. Oncol. 2008 26 2155 2161 10.1200/JCO.2007.14.5631 18362365

4. Hricak H. Abdel-Wahab M. Atun R. Lette M.M. Paez D. Brink J.A. Donoso-Bach L. Frija G. Hierath M. Holmberg O. Medical imaging and nuclear medicine: A Lancet Oncology Commission Lancet Oncol. 2021 10.1016/S1470-2045(20)30751-8 PMC8444235 33676609

5. Katsari K. Penna D. Arena V. Polverari G. Ianniello A. Italiano D. Milani R. Roncacci A. Illing R.O. Pelosi E. Artificial intelligence for reduced dose 18F-FDG PET examinations: A real-world deployment through a standardized framework and business case assessment EJNMMI Phys. 2021 8 25 10.1186/s40658-021-00374-7 33687602 PMC7943690

6. Boellaard R. Standards for PET image acquisition and quantitative data analysis J. Nucl. Med. 2009 50 11S 20S 10.2967/jnumed.108.057182 19380405

7. Aide N. Lasnon C. Veit-Haibach P. Sera T. Sattler B. Boellaard R. EANM/EARL harmonization strategies in PET quantification: From daily practice to multicentre oncological studies Eur. J. Nucl. Med. Mol. Imaging 2017 44 17 31 10.1007/s00259-017-3740-2 PMC5541084 28623376

8. Boellaard R. Delgado-Bolton R. Oyen W.J.G. Giammarile F. Tatsch K. Eschner W. Verzijlbergen F.J. Barrington S.F. Pike L.C. Weber W.A. FDG PET/CT: EANM procedure guidelines for tumour imaging: Version 2.0 Eur. J. Nucl. Med. Mol. Imaging 2015 42 328 354 10.1007/s00259-014-2961-x 25452219 PMC4315529

9. Bar-Sever Z. Biassoni L. Shulkin B. Kong G. Hofman M.S. Lopci E. Manea I. Koziorowski J. Castellani R. Boubaker A. Guidelines on nuclear medicine imaging in neuroblastoma Eur. J. Nucl. Med. Mol. Imaging 2018 45 2009 2024 10.1007/s00259-018-4070-8 29938300

10. Law I. Albert N.L. Arbizu J. Boellaard R. Drzezga A. Galldiks N. la Fougère C. Langen K.J. Lopci E. Lowe V. Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [18F]FDG: Version 1.0 Eur. J. Nucl. Med. Mol. Imaging 2019 46 540 557 10.1007/s00259-018-4207-9 30519867 PMC6351513

11. Shankar L.K. Hoffman J.M. Bacharach S. Graham M.M. Karp J. Lammertsma A.A. Larson S. Mankoff D.A. Siegel B.A. Abbeele A.V.D. Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute Trials J. Nucl. Med. 2006 47 1059 1066 16741317

12. Barrington S.F. Mikhaeel N.G. Kostakoglu L. Meignan M. Hutchings M. Müeller S.P. Schwartz L.H. Zucca E. Fisher R.I. Trotman J. Role of Imaging in the Staging and Response Assessment of Lymphoma: Consensus of the International Conference on Malignant Lymphomas Imaging Working Group J. Clin. Oncol. 2014 32 3048 3058 10.1200/JCO.2013.53.5229 25113771 PMC5015423

13. Wirth A. Foo M. Seymour J.F. MacManus M.P. Hicks R.J. Impact of [18F] Fluorodeoxyglucose Positron Emission Tomography on Staging and Management of Early-Stage Follicular Non-Hodgkin Lymphoma Int. J. Radiat. Oncol. Biol. Phys. 2008 71 213 219 10.1016/j.ijrobp.2007.09.051 18295982

14. Reinhardt M.J. Joe A.Y. Jaeger U. Hubetr A. Matthies A. Bucerius J. Roedel R. Strunk H. Bieber T. Biersack H.-J. Diagnostic Performance of Whole Body Dual Modality 18 F-FDG PET/CT Imaging for N- and M-Staging of Malignant Melanoma: Experience With 250 Consecutive Patients J. Clin. Oncol. 2006 24 1178 1187 10.1200/JCO.2005.03.5634 16505438

15. Gould M.K. Maclean C.C. Kuschner W.G. Rydzak C.E. Owens D.K. Accuracy of Positron Emission Tomography for Diagnosis of Pulmonary Nodules and Mass Lesions: A Meta-analysis JAMA 2001 285 914 10.1001/jama.285.7.914 11180735

16. Kolodziejczyk M. Kepka L. Dziuk M. Zawadzka A. Szalus N. Gizewska A. Bujko K. Impact of [18F]Fluorodeoxyglucose PET-CT Staging on Treatment Planning in Radiotherapy Incorporating Elective Nodal Irradiation for Non-Small-Cell Lung Cancer: A Prospective Study Int. J. Radiat. Oncol. Biol. Phys. 2011 80 1008 1014 10.1016/j.ijrobp.2010.04.018 20656419

17. King K.S. Chen C.C. Alexopoulos D.K. Whatlety M.A. Reynolds J.C. Patronas N. Ling A. Adams K.T. Xekouki P. Lando H. Functional Imaging of SDHx -Related Head and Neck Paragangliomas: Comparison of 18 F-Fluorodihydroxyphenylalanine, 18 F-Fluorodopamine, 18 F-Fluoro-2-Deoxy- d -Glucose PET, 123 I-Metaiodobenzylguanidine Scintigraphy, and 111 In-Pentetreotide Scintigraphy J. Clin. Endocrinol. Metab. 2011 96 2779 2785 10.1210/jc.2011-0333 21752889 PMC3167674

18. Treglia G. Cocciolillo F. de Waure C. Di Nardo F. Gualano M.R. Castaldi P. Rufini V. Giordano A. Diagnostic performance of 18F-dihydroxyphenylalanine positron emission tomography in patients with paraganglioma: A meta-analysis Eur. J. Nucl. Med. Mol. Imaging 2012 39 1144 1153 10.1007/s00259-012-2087-y 22358431

19. Taieb D. Neumann H. Rubello D. Al-Nahhas A. Guillet B. Hindie E. Modern Nuclear Imaging for Paragangliomas: Beyond SPECT J. Nucl. Med. 2012 53 264 274 10.2967/jnumed.111.098152 22302963

20. Zamagni E. Nanni C. Dozza L. Carlier T. Tacchetti P. Versari A. Chauvie S. Gallamini A. Attal M. Gamberi B. Standardization of 18F-FDG PET/CT According to Deauville Criteria for MRD Evaluation in Newly Diagnosed Transplant Eligible Multiple Myeloma Patients: Joined Analysis of Two Prospective Randomized Phase III Trials Blood 2018 132 257 10.1182/blood-2018-99-111321

21. Kubota K. Matsuzawa T. Ito M. Ito K. Fujiwara T. Albe Y. Yoshioka S. Fukuda H. Hatazawa J. Iwata R. Lung tumor imaging by positron emission tomography using C-11 L-methionine J. Nucl. Med. 1985 26 37 42 2981300

22. Thie J.A. Understanding the standardized uptake value, its methods, and implications for usage J. Nucl. Med. 2004 45 1431 1434 15347707

23. Boellaard R. Krak N.C. Hoekstra O.S. Lammertsma A.A. Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: A simulation study J. Nucl. Med. 2004 45 1519 1527 15347719

24. Foster B. Bagci U. Mansoor A. Xu Z. Mollura D.J. A review on segmentation of positron emission tomography images Comput. Biol. Med. 2014 50 76 96 10.1016/j.compbiomed.2014.04.014 24845019 PMC4060809

25. Ott K. Weber W.A. Lordick F. Becker K. Busch R. Herrmann K. Wieder H. Fink U. Schwaiger M. Siewert J.-R. Metabolic imaging predicts response, survival, and recurrence in adenocarcinomas of the esophagogastric junction J. Clin. Oncol. 2006 24 4692 4698 10.1200/JCO.2006.06.7801 16966684

26. Cascini G.L. Avallone A. Delrio P. Guida C. Tatangetlo F. Marone P. Aloj L. De Martinis F. Comella P. Parisi V. 18F-FDG PET is an early predictor of pathologic tumor response to preoperative radiochemotherapy in locally advanced rectal cancer J. Nucl. Med. 2006 47 1241 1248 16883000

27. Nols N. Mounier N. Bouazza S. Lhommel R. Costantini S. Borght T.V. Vekemans M.-C. Sonet A. Bosly A. Michaux L. Quantitative and qualitative analysis of metabolic response at interim positron emission tomography scan combined with International Prognostic Index is highly predictive of outcome in diffuse large B-cell lymphoma Leuk. Lymphoma 2014 55 773 780 10.3109/10428194.2013.831848 23927393

28. Lin C. Itti E. Haioun C. Petegnief Y. Luciani A. Dupuis J. Paone G. Talbot J.-N. Rahmouni A. Meignan M. Early 18F-FDG PET for Prediction of Prognosis in Patients with Diffuse Large B-Cell Lymphoma: SUV-Based Assessment Versus Visual Analysis J. Nucl. Med. 2007 48 1626 1632 10.2967/jnumed.107.042093 17873129

29. Fuertes S. Setoain X. Lopez-Guillermo A. Carrasco J.-L. Rodriguetz S. Roviral J. Pons F. Interim FDG PET/CT as a prognostic factor in diffuse large B-cell lymphoma Eur. J. Nucl. Med. Mol. Imaging 2013 40 496 504 10.1007/s00259-012-2320-8 23344136

30. Casasnovas R.-O. Meignan M. Berriolo-Riedinger A. Bardet S. Julian A. Thieblemont C. Vera P. Bologna S. Brière J. Jais J.-P. SUVmax reduction improves early prognosis value of interim positron emission tomography scans in diffuse large B-cell lymphoma Blood 2011 118 37 43 10.1182/blood-2010-12-327767 21518924

31. Stroobants S. Goeminne J. Seegers M. Dimitrijevic S. Dupont P. Nuyts J. Martens M. Borne B.V.D. Cole P. Sciot R. 18FDG-Positron emission tomography for the early prediction of response in advanced soft tissue sarcoma treated with imatinib mesylate (Glivec ® ) Eur. J. Cancer 2003 39 2012 2020 10.1016/S0959-8049(03)00073-X 12957455

32. Prior J.O. Montemurro M. Orcurto M.-V. Michielin O. Luthi F. Benhattar J. Guillou L. Elsig V. Stupp R. Delaloye A.B. Early Prediction of Response to Sunitinib After Imatinib Failure by 18 F-Fluorodeoxyglucose Positron Emission Tomography in Patients With Gastrointestinal Stromal Tumor J. Clin. Oncol. 2009 27 439 445 10.1200/JCO.2008.17.2742 19064982

33. Usmani S.Z. Mitchell A. Waheed S. Crowley J. Hoering A. Petty N. Brown T. Bartel T. Anaissie E. Van Rhee F. Prognostic implications of serial 18-fluoro-deoxyglucose emission tomography in multiple myeloma treated with total therapy 3 Blood 2013 121 1819 1823 10.1182/blood-2012-08-451690 23305732 PMC3591801

34. McDonald J.E. Kessler M.M. Gardner M.W. Buros A.F. Ntambi J.A. Waheed S. Van Rhee F. Zangari M. Heuck C.J. Petty N. Assessment of Total Lesion Glycolysis by 18 F FDG PET/CT Significantly Improves Prognostic Value of GEP and ISS in Myeloma Clin. Cancer Res. 2017 23 1981 1987 10.1158/1078-0432.CCR-16-0235 27698001 PMC5777601

35. Patlak C.S. Blasberg R.G. Graphical Evaluation of Blood-to-Brain Transfer Constants from Multiple-Time Uptake Data. Generalizations J. Cereb. Blood Flow. Metab. 1985 5 584 590 10.1038/jcbfm.1985.87 4055928

36. Morris E.D. Endres C.J. Schmidt K.C. Christian B.T. Muzic R.F. Fisher R.E. Kinetic Modeling in Positron Emission Tomography Emission Tomography Elsevier Amsterdam, The Netherlands 2004 499 540 10.1016/B978-012744482-6.50026-0

37. Dimitrakopoulou-Strauss A. Pan L. Sachpekidis C. Kinetic modeling and parametric imaging with dynamic PET for oncological applications: General considerations, current clinical applications, and future perspectives Eur. J. Nucl. Med. Mol. Imaging 2021 48 21 39 10.1007/s00259-020-04843-6 32430580 PMC7835173

38. Sokoloff L. Reivich M. Kennedy C. Rosiers M.H.D. Patlak C.S. Pettigrew K.D. Salkurada O. Shinohara M. The [ 14 C]Deoxyglucose Method for the Measurement of Local Cerebral Glucose Utilization: Theory, Procedure, and Normal Values in the Conscious and Anesthetized Albino rat J. Neurochem. 1977 28 897 916 10.1111/j.1471-4159.1977.tb10649.x 864466

39. Badawi R.D. Shi H. Hu P. Chen S. Xu T. Price P.M. Ding Y. Spencer B.A. Nardo L. Liu W. First Human Imaging Studies with the EXPLORER Total-Body PET Scanner J. Nucl. Med. 2019 60 299 303 10.2967/jnumed.119.226498 30733314 PMC6424228

40. Karp J.S. Viswanath V. Geagan M.J. Muehllehner G. Pantel A.R. Parma M.J. Perkins A.E. Schmall J.P. Werner M.E. Daube-Witherspoon M.E. PennPET Explorer: Design and Preliminary Performance of a Whole-Body Imager J. Nucl. Med. 2020 61 136 143 10.2967/jnumed.119.229997 31227573 PMC6954465

41. Zhang X. Xie Z. Berg E. Judenhofer M.S. Liu W. Xu T. Ding Y. Lv Y. Dong Y. Deng Z. Total-Body Dynamic Reconstruction and Parametric Imaging on the uEXPLORER J. Nucl. Med. 2020 61 285 291 10.2967/jnumed.119.230565 31302637 PMC8801950

42. Mullani N.A. Herbst R.S. O’Neil R.G. Gould K.L. Barron B.J. Abbruzzese J.L. Tumor Blood Flow Measured by PET Dynamic Imaging of First-Pass 18F-FDG Uptake: A Comparison with 15O-Labeled Water-Measured Blood Flow J. Nucl. Med. 2008 49 517 523 10.2967/jnumed.107.048504 18344436

43. Cochet A. Pigeonnat S. Khoury B. Vrigneaud J.-M. Touzery C. Berriolo-Riedinger A. Dygai-Cochet I. Toubeau M. Humbert O. Coudert B. Evaluation of Breast Tumor Blood Flow with Dynamic First-Pass 18F-FDG PET/CT: Comparison with Angiogenesis Markers and Prognostic Factors J. Nucl. Med. 2012 53 512 520 10.2967/jnumed.111.096834 22343501

44. Meikle S.R. Sossi V. Roncali E. Cherry S.R. Banati R. Mankoff D.A. Jones T. James M.L. Sutcliffe J. Ouyang J. Quantitative PET in the 2020s: A Roadmap Phys. Med. Biol. 2020 10.1088/1361-6560/abd4f7 PMC9358699 33339012

45. Eisenhauer E.A. Therasse P. Bogaerts J. Schwartz L.H. Sargent D. Ford R. Dancey J. Arbuck S. Gwyther S. Mooney M. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1) Eur. J. Cancer 2009 45 228 247 10.1016/j.ejca.2008.10.026 19097774

46. Young H. Baum R. Cremerius U. Herholz K. Hoekstra O. Lammertsma A. Pruim J. Price P. Measurement of clinical and subclinical tumour response using [ 18 F]-fluorodeoxyglucose and positron emission tomography: Review and 1999 EORTC recommendations Eur. J. Cancer 1999 35 1773 1782 10.1016/S0959-8049(99)00229-4 10673991

47. Tylski P. Stute S. Grotus N. Doyeux K. Hapdey S. Gardin I. Vanderlinden B. Buvat I. Comparative assessment of methods for estimating tumor volume and standardized uptake value in (18)F-FDG PET J. Nucl. Med. 2010 51 268 276 10.2967/jnumed.109.066241 20080896

48. Wahl R.L. Jacene H. Kasamon Y. Lodge M.A. From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors J. Nucl. Med. 2009 50 122S 150S 10.2967/jnumed.108.057307 19403881 PMC2755245

49. Riedl C.C. Pinker K. Ulaner G.A. Ong L.T. Baltzer P. Jochelson M.S. McArthur H.L. Gönen M. Dickler M. Weber W.A. Comparison of FDG-PET/CT and contrast-enhanced CT for monitoring therapy response in patients with metastatic breast cancer Eur. J. Nucl. Med. Mol. Imaging 2017 44 1428 1437 10.1007/s00259-017-3703-7 28462446 PMC5526620

50. Yanagawa M. Tatsumi M. Miyata H. Morii E. Tomiyama N. Waltabe T. Isohashi K. Kato H. Shimosegawa E. Yamasaki M. Evaluation of Response to Neoadjuvant Chemotherapy for Esophageal Cancer: PET Response Criteria in Solid Tumors Versus Response Evaluation Criteria in Solid Tumors J. Nucl. Med. 2012 53 872 880 10.2967/jnumed.111.098699 22582049

51. Koshkin V.S. Bolejack V. Schwartz L.H. Wahl R.L. Chugh R. Reinke D.K. Zhao B. O J.H. Patel S.R. Schuetze S.M. Assessment of Imaging Modalities and Response Metrics in Ewing Sarcoma: Correlation with Survival J. Clin. Oncol. 2016 34 3680 3685 10.1200/JCO.2016.68.1858 27573658 PMC5065114

52. Shang J. Ling X. Zhang L. Tang Y. Xiao Z. Chetng Y. Guo B. Gong J. Huang L. Xu H. Comparison of RECIST, EORTC criteria and PERCIST for evaluation of early response to chemotherapy in patients with non-small-cell lung cancer Eur. J. Nucl. Med. Mol. Imaging 2016 43 1945 1953 10.1007/s00259-016-3420-7 27236466

53. Ding Q. Cheng X. Yang L. Zhang Q. Chen J. Li T. Shi H. PET/CT evaluation of response to chemotherapy in non-small cell lung cancer: PET response criteria in solid tumors (PERCIST) versus response evaluation criteria in solid tumors (RECIST) J. Thorac. Dis. 2014 6 677 683 10.3978/j.issn.2072-1439.2014.05.10 24976990 PMC4073366

54. Champiat S. Dercle L. Ammari S. Massard C. Hollebecque A. Postel-Vinay S. Chaput N. Eggermont A.M. Marabelle A. Soria J.-C. Hyperprogressive Disease Is a New Pattern of Progression in Cancer Patients Treated by Anti-PD-1/PD-L1 Clin. Cancer Res. 2017 23 1920 1928 10.1158/1078-0432.CCR-16-1741 27827313

55. Goldfarb L. Duchemann B. Chouahnia K. Zelek L. Soussan M. Monitoring anti-PD-1-based immunotherapy in non-small cell lung cancer with FDG PET: Introduction of iPERCIST EJNMMI Res. 2019 9 8 10.1186/s13550-019-0473-1 30694399 PMC6890907

56. Cho S.Y. Lipson E.J. Im H.-J. Rowet S.P. Gonzalez E.M. Blackford A. Chirindel A. Pardoll D.M. Topalian S.L. Wahl R.L. Prediction of Response to Immune Checkpoint Inhibitor Therapy Using Early-Time-Point 18 F-FDG PET/CT Imaging in Patients with Advanced Melanoma J. Nucl. Med. 2017 58 1421 1428 10.2967/jnumed.116.188839 28360208 PMC5577627

57. Anwar H. Sachpekidis C. Winkler J. Kopp-Schnetider A. Haberkorn U. Hassel J.C. Dimitrakopoulou-Strauss A. Absolute number of new lesions on 18F-FDG PET/CT is more predictive of clinical response than SUV changes in metastatic melanoma patients receiving ipilimumab Eur. J. Nucl. Med. Mol. Imaging 2018 45 376 383 10.1007/s00259-017-3870-6 29124281

58. Sachpekidis C. Anwar H. Winkler J. Kopp-Schnetider A. Larribere L. Haberkorn U. Hassel J.C. Dimitrakopoulou-Strauss A. The role of interim 18F-FDG PET/CT in prediction of response to ipilimumab treatment in metastatic melanoma Eur. J. Nucl. Med. Mol. Imaging 2018 45 1289 1296 10.1007/s00259-018-3972-9 29478079

59. Aide N. Hicks R.J. Le Tourneau C. Lheureux S. Fanti S. Lopci E. FDG PET/CT for assessing tumour response to immunotherapy: Report on the EANM symposium on immune modulation and recent review of the literature Eur. J. Nucl. Med. Mol. Imaging 2019 46 238 250 10.1007/s00259-018-4171-4 30291373 PMC6267687

60. Martin de Fremont G. Belkhir R. Henry J. Voisin A.L. Lambotte O. Besson F.L. Mariette X. Nocturne G. Features of polymyalgia rheumatica–like syndrome after immune checkpoint inhibitor therapy Ann. Rheum. Dis. 2020 10.1136/annrheumdis-2020-217225 32188691

61. Seban R.-D. Nemer J.S. Marabelle A. Yeh R. Deutsch E. Ammari S. Moya-Plana A. Mokrane F.-Z. Gartrell R.D. Finkel G. Prognostic and theranostic 18F-FDG PET biomarkers for anti-PD1 immunotherapy in metastatic melanoma: Association with outcome and transcriptomics Eur. J. Nucl. Med. Mol. Imaging 2019 46 2298 2310 10.1007/s00259-019-04411-7 31346755

62. Friedman C.F. Proverbs-Singh T.A. Postow M.A. Treatment of the Immune-Related Adverse Effects of Immune Checkpoint Inhibitors: A Review JAMA Oncol. 2016 2 1346 10.1001/jamaoncol.2016.1051 27367787

63. Haratani K. Hayashi H. Chiba Y. Kudo K. Yonesaka K. Kato R. Kalneda H. Hasegawa Y. Tanaka K. Takeda M. Association of Immune-Related Adverse Events with Nivolumab Efficacy in Non–Small-Cell Lung Cancer JAMA Oncol. 2018 4 374 10.1001/jamaoncol.2017.2925 28975219 PMC6583041

64. Cheson B.D. Pfistner B. Juweid M.E. Gascoyne R.D. Specht L. Horning S.J. Coiffier B. Fisher R.I. Hagenbeek A. Zucca E. Revised Response Criteria for Malignant Lymphoma J. Clin. Oncol. 2007 25 579 586 10.1200/JCO.2006.09.2403 17242396

65. Cheson B.D. Horning S.J. Coiffier B. Shipp M.A. Fisher R.I. Connors J.M. Lister T.A. Vose J. Grillo-López A. Hagenbeek A. Report of an International Workshop to Standardize Response Criteria for Non-Hodgkin’s Lymphomas J. Clin. Oncol. 1999 17 1244 10.1200/JCO.1999.17.4.1244 10561185

66. Cheson B.D. Fisher R.I. Barrington S.F. Cavalli F. Schwartz L.H. Zucca E. Lister T.A. Recommendations for Initial Evaluation, Staging, and Response Assessment of Hodgkin and Non-Hodgkin Lymphoma: The Lugano Classification J. Clin. Oncol. 2014 32 3059 3067 10.1200/JCO.2013.54.8800 25113753 PMC4979083

67. Itti E. Meignan M. Berriolo-Riedinger A. Biggi A. Cashen A.F. Véra P. Tilly H. Siegel B.A. Gallamini A. Casasnovas R.-O. An international confirmatory study of the prognostic value of early PET/CT in diffuse large B-cell lymphoma: Comparison between Deauville criteria and ΔSUVmax Eur. J. Nucl. Med. Mol. Imaging 2013 40 1312 1320 10.1007/s00259-013-2435-6 23649463

68. Yuan L. Kreissl M.C. Su L. Wu Z. Hacker M. Liu J. Zhang X. Bo Y. Zhang H. Li X. Prognostic Analysis of Interim 18F-FDG PET/CT in Patients with Diffuse Large B Cell Lymphoma after One Cycle versus Two Cycles of Chemotherapy Eur. J. Nucl. Med. Mol. Imaging 2019 46 478 488 10.1007/s00259-018-4198-6 30382301 PMC6333726

69. Schöder H. Polley M.-Y.C. Knopp M.V. Hall N.C. Kostakoglu L. Zhang J. Higley H.R. Kelloff G. Liu H. Zelenetz A.D. Prognostic value of interim FDG-PET in diffuse large cell lymphoma: Results from the CALGB 50303 Clinical Trial Blood 2020 135 2224 2234 10.1182/blood.2019003277 32232481 PMC7316220

70. Casasnovas R.-O. Ysebaert L. Thieblemont C. Bachy E. Feugier P. Delmer A. Tricot S. Gabarre J. Andre M. Fruchart C. FDG-PET–driven consolidation strategy in diffuse large B-cell lymphoma: Final results of a randomized phase 2 study Blood 2017 130 1315 1326 10.1182/blood-2017-02-766691 28701367

71. Dührsen U. Müller S. Hertenstein B. Thomssen H. Kotzerke J. Mesters R. Berdel W.E. Franzius C. Kroschinsky F. Weckesser M. Positron Emission Tomography–Guided Therapy of Aggressive Non-Hodgkin Lymphomas (PETAL): A Multicenter, Randomized Phase III Trial J. Clin. Oncol. 2018 36 2024 2034 10.1200/JCO.2017.76.8093 29750632

72. Cheson B.D. Ansell S. Schwartz L. Gordon L.I. Aldvani R. Jacene H.A. Hoos A. Barrington S.F. Armand P. Refinement of the Lugano Classification lymphoma response criteria in the era of immunomodulatory therapy Blood 2016 128 2489 2496 10.1182/blood-2016-05-718528 27574190

73. Younes A. Hilden P. Coiffier B. Hagenbeek A. Salles G. Wilson W. Seymour J.F. Kelly K. Gribben J. Pfreunschuh M. International Working Group consensus response evaluation criteria in lymphoma (RECIL 2017) Ann. Oncol. 2017 28 1436 1447 10.1093/annonc/mdx097 28379322 PMC5834038

74. Beksac M. Gunduz M. Ozen M. Bakanay Ozturk S.M. Kucuk O. Ozkan E. Impact of PET-CT Response on Survival Parameters Following Autologous Stem Cell Transplantation Among Patients with Multiple Myeloma: Comparison of Two Cut-Off Values Blood 2014 124 3983 10.1182/blood.V124.21.3983.3983

75. Rajkumar S.V. Dimopoulos M.A. Palumbo A. Blade J. Merlini G. Mateos M.-V. Kumar S. Hillengass J. Kastritis E. Richardson P. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma Lancet Oncol. 2014 15 e538 e548 10.1016/S1470-2045(14)70442-5 25439696

76. Fonti R. Larobina M. Del Vecchio S. De Luca S. Fabbricini R. Catalano L. Pane F. Salvatore M. Pace L. Metabolic Tumor Volume Assessed by 18F-FDG PET/CT for the Prediction of Outcome in Patients with Multiple Myeloma J. Nucl. Med. 2012 53 1829 1835 10.2967/jnumed.112.106500 23071351

77. Nanni C. Zamagni E. Versari A. Chauviet S. Bianchi A. Rensi M. Bellò M. Rambaldi I. Gallamini A. Patriarca F. Image interpretation criteria for FDG PET/CT in multiple myeloma: A new proposal from an Italian expert panel. IMPeTUs (Italian Myeloma criteria for PET USe) Eur. J. Nucl. Med. Mol. Imaging 2016 43 414 421 10.1007/s00259-015-3200-9 26475305

78. Modlin I.M. Lye K.D. Kidd M. A 5-decade analysis of 13,715 carcinoid tumors Cancer 2003 97 934 959 10.1002/cncr.11105 12569593

79. Krenning E.P. Breeman W.A.P. Kooij P.P.M. Lameris J. Bakketr W. Koper J. Ausema L. Reubi J. Lamberts S. Localisation of endocrine-related tumours with radioiodinated analogue of somatostatin Lancet 1989 333 242 244 10.1016/S0140-6736(89)91258-0 2563413

80. Krenning E.P. Kwekkeboom D.J. Bakker W.H. Van Hagen M. Postema P. De Jong M. Reubi J.-C. Visser T. Reijs A. Hofland L. Somatostatin receptor scintigraphy with [111In-DTPA-d-Phe1]- and [123I-Tyr3]-octreotide: The Rotterdam experience with more than 1000 patients Eur. J. Nucl. Med. 1993 20 716 731 10.1007/BF00181765 8404961

81. Hofmann M. Maecke H. Börner A. Wetckesser E. Schöffski P. Oei M. Schumacher J. Henze M. Heppeler A. Meyer J. Biokinetics and imaging with the somatostatin receptor PET radioligand 68Ga-DOTATOC: Preliminary data Eur. J. Nucl. Med. 2001 28 1751 1757 10.1007/s002590100639 11734911

82. Poeppel T.D. Binse I. Petersenn S. Lahner H. Schott M. Alntoch G. Brandau W. Bockisch A. Boy C. 68Ga-DOTATOC Versus 68Ga-DOTATATE PET/CT in Functional Imaging of Neuroendocrine Tumors J. Nucl. Med. 2011 52 1864 1870 10.2967/jnumed.111.091165 22072704

83. Velikyan I. Sundin A. Sorensen J. Lubberink M. Sandström M. Garske-Román U. Lundqvist H. Granberg D. Eriksson B. Quantitative and Qualitative Intrapatient Comparison of 68Ga-DOTATOC and 68Ga-DOTATATE: Net Uptake Rate for Accurate Quantification J. Nucl. Med. 2014 55 204 210 10.2967/jnumed.113.126177 24379222

84. Geijer H. Breimer L.H. Somatostatin receptor PET/CT in neuroendocrine tumours: Update on systematic review and meta-analysis Eur. J. Nucl. Med. Mol. Imaging 2013 40 1770 1780 10.1007/s00259-013-2482-z 23873003

85. Van Binnebeek S. Vanbilloen B. Baete K. Terwinghe C. Koole M. Mottaghy F.M. Clement P.M. Mortelmans L. Bogaerts K. Haustermans K. Comparison of diagnostic accuracy of 111In-pentetreotide SPECT and 68Ga-DOTATOC PET/CT: A lesion-by-lesion analysis in patients with metastatic neuroendocrine tumours Eur. Radiol. 2016 26 900 909 10.1007/s00330-015-3882-1 26162577

86. Sundin A. Arnold R. Baudin E. Cwikla J.B. Eriksson B. Fanti S. Fazio N. Giammarile F. Hicks R.J. Kjaer A. ENETS Consensus Guidelines for the Standards of Care in Neuroendocrine Tumors: Radiological, Nuclear Medicine and Hybrid Imaging Neuroendocrinology 2017 105 212 244 10.1159/000471879 28355596

87. Shah M.H. Goldner W.S. Halfdanarson T.R. Bergsland E. Berlin J.D. Halperin D. Chan J. Kulke M.H. Benson A.B. Blaszkowsky L.S. NCCN Guidelines Insights: Neuroendocrine and Adrenal Tumors, Version 2.2018 J. Natl. Compr. Cancer Netw. 2018 16 693 702 10.6004/jnccn.2018.0056 29891520

88. Strosberg J. El-Haddad G. Wolin E. Hendifar A. Yao J. Chasen B. Mittra E. Kunz P.L. Kulke M.H. Jacene H. Phase 3 Trial of 177 Lu-Dotatate for Midgut Neuroendocrine Tumors N. Engl. J. Med. 2017 376 125 135 10.1056/NEJMoa1607427 28076709 PMC5895095

89. Garcia-Carbonero R. Sorbye H. Baudin E. Raymond E. Wiedenmann B. Niederle B. Sedlackova E. Toumpanakis C. Anlauf M. Cwikla J.B. ENETS Consensus Guidelines for High-Grade Gastroenteropancreatic Neuroendocrine Tumors and Neuroendocrine Carcinomas Neuroendocrinology 2016 103 186 194 10.1159/000443172 26731334

90. Basturk O. Yang Z. Tang L.H. Hruban R.H. Adsay V. McCall C.M. Krasinskas A.M. Jang K.-T. Frankel W.L. Balci S. The High-grade (WHO G3) Pancreatic Neuroendocrine Tumor Category Is Morphologically and Biologically Heterogenous and Includes Both Well Differentiated and Poorly Differentiated Neoplasms Am. J. Surg. Pathol. 2015 39 683 690 10.1097/PAS.0000000000000408 25723112 PMC4398606

91. Demirci E. Kabasakal L. Toklu T. Ocak M. Şahin O.E. Alan-Selcuk N. Araman A. 177Lu-DOTATATE therapy in patients with neuroendocrine tumours including high-grade (WHO G3) neuroendocrine tumours: Response to treatment and long-term survival update Nucl. Med. Commun. 2018 39 789 796 10.1097/MNM.0000000000000874 29912750

92. Severi S. Nanni O. Bodei L. Sansovini M. Ianniello A. Nicoletti S. Scarpi E. Matteucci F. Gilardi L. Paganelli G. Role of 18FDG PET/CT in patients treated with 177Lu-DOTATATE for advanced differentiated neuroendocrine tumours Eur. J. Nucl. Med. Mol. Imaging 2013 40 881 888 10.1007/s00259-013-2369-z 23443937

93. Bahri H. Laurence L. Edeline J. Leghzali H. Devillers A. Raoul J.-L. Cuggia M. Mesbah H. Clement B. Boucher E. High Prognostic Value of 18F-FDG PET for Metastatic Gastroenteropancreatic Neuroendocrine Tumors: A Long-Term Evaluation J. Nucl. Med. 2014 55 1786 1790 10.2967/jnumed.114.144386 25286923

94. Bray F. Ferlay J. Soerjomataram I. Siegel R.L. Torre L.A. Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J. Clin. 2018 68 394 424 10.3322/caac.21492 30207593

95. Evangelista L. Zattoni F. Guttilla A. Saladini G. Zattoni F. Colletti P.M. Rubello D. Choline PET or PET/CT and Biochemical Relapse of Prostate Cancer: A Systematic Review and Meta-Analysis Clin. Nucl. Med. 2013 38 305 314 10.1097/RLU.0b013e3182867f3c 23486334

96. Chiaravalloti A. Di Biagio D. Tavolozza M. Calabria F. Schillaci O. PET/CT with 18F-choline after radical prostatectomy in patients with PSA ≤2 ng/ml. Can PSA velocity and PSA doubling time help in patient selection? Eur. J. Nucl. Med. Mol. Imaging 2016 43 1418 1424 10.1007/s00259-015-3306-0 26791373

97. Graziani T. Ceci F. Castellucci P. Polverari G. Lima G.M. Lodi F. Morganti A.G. Ardizzoni A. Schiavina R. Fanti S. 11C-Choline PET/CT for restaging prostate cancer. Results from 4,426 scans in a single-centre patient series Eur. J. Nucl. Med. Mol. Imaging 2016 43 1971 1979 10.1007/s00259-016-3428-z 27277279

98. Silver D.A. Pellicer I. Fair W.R. Heston W.D. Cordon-Cardo C. Prostate-specific membrane antigen expression in normal and malignant human tissues Clin. Cancer Res. 1997 3 81 85 9815541

99. Sweat S.D. Pacelli A. Murphy G.P. Bostwick D.G. Prostate-specific membrane antigen expression is greatest in prostate adenocarcinoma and lymph node metastases Urology 1998 52 637 640 10.1016/S0090-4295(98)00278-7 9763084

100. Mannweiler S. Amersdorfer P. Trajanoski S. Terrett J.A. King D. Mehes G. Heterogeneity of Prostate-Specific Membrane Antigen (PSMA) Expression in Prostate Carcinoma with Distant Metastasis Pathol. Oncol. Res. 2009 15 167 172 10.1007/s12253-008-9104-2 18802790

101. Hövels A.M. Heesakkers R.A.M. Adang E.M. Jager G. Strum S. Hoogeveen Y. Severens J. Barentsz J. The diagnostic accuracy of CT and MRI in the staging of pelvic lymph nodes in patients with prostate cancer: A meta-analysis Clin. Radiol. 2008 63 387 395 10.1016/j.crad.2007.05.022 18325358

102. Bakht M.K. Derecichei I. Li Y. Ferraiuolo R.-M. Dunning M.J. Oh S.W. Hussein A. Youn H. Stringer K.F. Jeong C.W. Neuroendocrine differentiation of prostate cancer leads to PSMA suppression Endocr.-Relat. Cancer 2019 26 131 146 10.1530/ERC-18-0226 30400059

103. Tan N. Bavadian N. Calais J. Oyoyo U. Kim J. Turkbey I.B. Mena E. Davenport M.S. Imaging of Prostate Specific Membrane Antigen Targeted Radiotracers for the Detection of Prostate Cancer Biochemical Recurrence after Definitive Therapy: A Systematic Review and Meta-Analysis J. Urol. 2019 202 231 240 10.1097/JU.0000000000000198 30829130

104. Evans J.D. Jethwa K.R. Ost P. Williams S. Kwon E.D. Lowe V.J. Davis B.J. Prostate cancer–specific PET radiotracers: A review on the clinical utility in recurrent disease Pract. Radiat. Oncol. 2018 8 28 39 10.1016/j.prro.2017.07.011 29037965

105. Tan N. Oyoyo U. Bavadian N. Ferguson N. Mukkamala A. Calais J. Davenport M.S. PSMA-targeted Radiotracers versus 18 F Fluciclovine for the Detection of Prostate Cancer Biochemical Recurrence after Definitive Therapy: A Systematic Review and Meta-Analysis Radiology 2020 296 44 55 10.1148/radiol.2020191689 32396045

106. Hope T.A. Goodman J.Z. Allen I.E. Calais J. Fendler W.P. Carroll P.R. Metaanalysis of 68 Ga-PSMA-11 PET Accuracy for the Detection of Prostate Cancer Validated by Histopathology J. Nucl. Med. 2019 60 786 793 10.2967/jnumed.118.219501 30530831 PMC6581235

107. Ferraro D.A. Becker A.S. Kranzbühler B. Mebert I. Baltensperger A. Zeimpekis K.G. Grünig H. Messerli M. Rupp N.J. Rueschoff J.H. Diagnostic performance of 68Ga-PSMA-11 PET/MRI-guided biopsy in patients with suspected prostate cancer: A prospective single-center study Eur. J. Nucl. Med. Mol. Imaging 2021 10.1007/s00259-021-05261-y PMC8426229 33620559

108. Fendler W.P. Schmidt D.F. Wenter V. Thierfelder K.M. Zach C. Stief C. Bartenstein P. Kirchner T. Gildehaus F.J. Gratzke C. 68Ga-PSMA PET/CT Detects the Location and Extent of Primary Prostate Cancer J. Nucl. Med. 2016 57 1720 1725 10.2967/jnumed.116.172627 27261520

109. Cytawa W. Seitz A.K. Kircher S. Fukushima K. Tran-Gia J. Schirbetl A. Bandurski T. Lass P. Krebs M. Połom W. 68Ga-PSMA I&T PET/CT for primary staging of prostate cancer Eur. J. Nucl. Med. Mol. Imaging 2020 47 168 177 10.1007/s00259-019-04524-z 31529265

110. Ma T.M. Gafita A. Shabsovich D. Juarez J. Grogan T.R. Thin P. Armstrong W. Sonni I. Nguyen K. Lok V. Identifying the Best Candidates for Prostate-specific Membrane Antigen Positron Emission Tomography/Computed Tomography as the Primary Staging Approach among Men with High-risk Prostate Cancer and Negative Conventional Imaging Eur. Urol. Oncol. 2021 10.1016/j.euo.2021.01.006 PMC10262977 33602654

111. Fendler W.P. Calais J. Eiber M. Flavell R.R. Mishoe A. Feng F.Y. Nguyen H.G. Reiter R.E. Rettig M.B. Okamoto S. Assessment of 68 Ga-PSMA-11 PET Accuracy in Localizing Recurrent Prostate Cancer: A Prospective Single-Arm Clinical Trial JAMA Oncol. 2019 5 856 10.1001/jamaoncol.2019.0096 30920593 PMC6567829

112. Calais J. Ceci F. Eiber M. Hope T.A. Hofman M.S. Rischpler C. Bach-Gansmo T. Nanni C. Savir-Baruch B. Elashoff D. 18F-fluciclovine PET-CT and 68Ga-PSMA-11 PET-CT in patients with early biochemical recurrence after prostatectomy: A prospective, single-centre, single-arm, comparative imaging trial Lancet Oncol. 2019 20 1286 1294 10.1016/S1470-2045(19)30415-2 31375469 PMC7469487

113. Fendler W.P. Ferdinandus J. Czernin J. Eiber M. Flavell R.R. Behr S.C. Wu I.-W.K. Lawhn-Heath C. Pampaloni M.H. Reiter R.E. Impact of 68 Ga-PSMA-11 PET on the Management of Recurrent Prostate Cancer in a Prospective Single-Arm Clinical Trial J. Nucl. Med. 2020 61 1793 1799 10.2967/jnumed.120.242180 32358094 PMC9364898

114. Hofman M.S. Lawrentschuk N. Francis R.J. Tang C. Vela I. Thomas P. Rutherford N. Martin J.M. Frydenberg M. Shakher R. Prostate-specific membrane antigen PET-CT in patients with high-risk prostate cancer before curative-intent surgery or radiotherapy (proPSMA): A prospective, randomised, multicentre study Lancet 2020 395 1208 1216 10.1016/S0140-6736(20)30314-7 32209449

115. Rowe S.P. Macura K.J. Mena E. Blackford A.L. Nadal R. Antonarakis E.S. Eisenberger M. Carducci M. Fan H. Dannals R.F. PSMA-Based [18F]DCFPyL PET/CT Is Superior to Conventional Imaging for Lesion Detection in Patients with Metastatic Prostate Cancer Mol. Imaging Biol. 2016 18 411 419 10.1007/s11307-016-0957-6 27080322 PMC5261857

116. Kelly J. Amor-Coarasa A. Nikolopoulou A. Kim D. Williams C. Ponnala S. Babich J.W. Synthesis and pre-clinical evaluation of a new class of high-affinity 18F-labeled PSMA ligands for detection of prostate cancer by PET imaging Eur. J. Nucl. Med. Mol. Imaging 2017 44 647 661 10.1007/s00259-016-3556-5 27847991 PMC5323493

117. Cardinale J. Schäfer M. Benešová M. Bauder-Wüst U. Leotta K. Eder M. Neels O.C. Haberkorn U. Giesel F.L. Kopka K. Preclinical Evaluation of 18 F-PSMA-1007, a New Prostate-Specific Membrane Antigen Ligand for Prostate Cancer Imaging J. Nucl. Med. 2017 58 425 431 10.2967/jnumed.116.181768 27789722

118. Harada N. Kimura H. Onoe S. Watanabe H. Matsuoka D. Alrimitsu K. Ono M. Saji H. Synthesis and Biologic Evaluation of Novel 18F-Labeled Probes Targeting Prostate-Specific Membrane Antigen for PET of Prostate Cancer J. Nucl. Med. 2016 57 1978 1984 10.2967/jnumed.116.175810 27417647

119. Jones W. Griffiths K. Barata P.C. Paller C.J. PSMA Theranostics: Review of the Current Status of PSMA-Targeted Imaging and Radioligand Therapy Cancers 2020 12 1367 10.3390/cancers12061367 PMC7352725 32466595

120. Hofman M.S. Violet J. Hicks R.J. Ferdinandus J. Thang S.P. Akhurst T. Iravani A. Kong G. Kumar A.R. Murphy D.G. [177 Lu]-PSMA-617 radionuclide treatment in patients with metastatic castration-resistant prostate cancer (LuPSMA trial): A single-centre, single-arm, phase 2 study Lancet Oncol. 2018 19 825 833 10.1016/S1470-2045(18)30198-0 29752180

121. Hofman M.S. Emmett L. Sandhu S. Iravani A. Joshua A.M. Goh J.C. Pattison D.A. Tan T.H. Kirkwood I.D. Ng S. [177Lu]Lu-PSMA-617 versus cabazitaxel in patients with metastatic castration-resistant prostate cancer (TheraP): A randomised, open-label, phase 2 trial Lancet 2021 397 797 804 10.1016/S0140-6736(21)00237-3 33581798

122. Ceci F. Oprea-Lager D.E. Emmett L. Adam J.A. Bomanji J. Czernin J. Eiber M. Haberkorn U. Hofman M.S. Hope T.A. E-PSMA: The EANM standardized reporting guidelines v1.0 for PSMA-PET Eur. J. Nucl. Med. Mol. Imaging 2021 10.1007/s00259-021-05245-y PMC8113168 33604691

123. Chiro G.D. Positron Emission Tomography Using [18F] Fluorodeoxyglucose in Brain Tumors A Powerful Diagnostic and Prognostic Tool Investig. Radiol. 1987 22 360 371 10.1097/00004424-198705000-00002 3496318

124. Vander Heiden M.G. Cantley L.C. Thompson C.B. Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation Science 2009 324 1029 1033 10.1126/science.1160809 19460998 PMC2849637

125. Farwell M.D. Pryma D.A. Mankoff D.A. PET/CT imaging in cancer: Current applications and future directions: PET-CT Imaging in Cancer Cancer 2014 120 3433 3445 10.1002/cncr.28860 24947987

126. Hanahan D. Weinberg R.A. Hallmarks of Cancer: The Next Generation Cell 2011 144 646 674 10.1016/j.cell.2011.02.013 21376230

127. West H. Immune Checkpoint Inhibitors JAMA Oncol. 2015 1 115 10.1001/jamaoncol.2015.0137 26182315

128. Binnewies M. Roberts E.W. Kersten K. Chan V. Fearon D.F. Merad M. Coussens L.M. Gabrilovich D.I. Ostrand-Rosenberg S. Hedrick C.C. Understanding the tumor immune microenvironment (TIME) for effective therapy Nat. Med. 2018 24 541 550 10.1038/s41591-018-0014-x 29686425 PMC5998822

129. Haslam A. Prasad V. Estimation of the Percentage of US Patients With Cancer Who Are Eligible for and Respond to Checkpoint Inhibitor Immunotherapy Drugs JAMA Netw. Open 2019 2 e192535 10.1001/jamanetworkopen.2019.2535 31050774 PMC6503493

130. Valero C. Lee M. Hoen D. Zehir A. Berger M.F. Seshan V.E. Chan T.A. Morris L.G.T. Response Rates to Anti–PD-1 Immunotherapy in Microsatellite-Stable Solid Tumors with 10 or More Mutations per Megabase JAMA Oncol. 2021 10.1001/jamaoncol.2020.7684 PMC7893543 33599686

131. Haslam A. Gill J. Prasad V. Estimation of the Percentage of US Patients With Cancer Who Are Eligible for Immune Checkpoint Inhibitor Drugs JAMA Netw. Open 2020 3 e200423 10.1001/jamanetworkopen.2020.0423 32150268 PMC7063495

132. Datta M. Coussens L.M. Nishikawa H. Hodi F.S. Jain R.K. Reprogramming the Tumor Microenvironment to Improve Immunotherapy: Emerging Strategies and Combination Therapies Am. Soc. Clin. Oncol. Educ. Book 2019 39 165 174 10.1200/EDBK_237987 31099649 PMC6596289

133. Popel A.S. Immunoactivating the tumor microenvironment enhances immunotherapy as predicted by integrative computational model Proc. Natl. Acad. Sci. USA 2020 117 4447 4449 10.1073/pnas.2001050117 32102915 PMC7060734

134. Bouleau A. Lebon V. Truillet C. PET imaging of immune checkpoint proteins in oncology Pharmacol. Ther. 2021 222 107786 10.1016/j.pharmthera.2020.107786 33307142

135. Bensch F. van der Veen E.L. Lub-de Hooge M.N. Jorritsma-Smit A. Boellaard R. Kok I.C. Oosting S.F. Schröder C.P. Hiltermann T.J.N. Van Der Wekken A.J. 89Zr-atezolizumab imaging as a non-invasive approach to assess clinical response to PD-L1 blockade in cancer Nat. Med. 2018 24 1852 1858 10.1038/s41591-018-0255-8 30478423

136. Niemeijer A.N. Leung D. Huisman M.C. Bahce I. Hoekstra O.S. Van Dongen G.A.M.S. Boellaard R. Du S. Hayes W. Smith R. Whole body PD-1 and PD-L1 positron emission tomography in patients with non-small-cell lung cancer Nat. Commun. 2018 9 4664 10.1038/s41467-018-07131-y 30405135 PMC6220188

137. Loktev A. Lindner T. Mier W. Debus J. Altmann A. Jäger D. Giesel F. Kratochwil C. Barthe P. Roumestand C. A Tumor-Imaging Method Targeting Cancer-Associated Fibroblasts J. Nucl. Med. 2018 59 1423 1429 10.2967/jnumed.118.210435 29626120 PMC6126438

138. Giesel F.L. Kratochwil C. Lindner T. Marschalek M.M. Loktev A. Lehnert W. Debus J. Jäger D. Flechsig P. Altmann A. 68 Ga-FAPI PET/CT: Biodistribution and Preliminary Dosimetry Estimate of 2 DOTA-Containing FAP-Targeting Agents in Patients with Various Cancers J. Nucl. Med. 2019 60 386 392 10.2967/jnumed.118.215913 30072500 PMC6424229

139. Kratochwil C. Flechsig P. Lindner T. Abderrahim L. Altmann A. Mier W. Adeberg S. Rathke H. Röhrich M. Winter H. 68 Ga-FAPI PET/CT: Tracer Uptake in 28 Different Kinds of Cancer J. Nucl. Med. 2019 60 801 805 10.2967/jnumed.119.227967 30954939 PMC6581228

140. Barrett R.L. Puré E. Cancer-associated fibroblasts and their influence on tumor immunity and immunotherapy eLife 2020 9 e57243 10.7554/eLife.57243 33370234 PMC7769568

141. Chen X. Song E. Turning foes to friends: Targeting cancer-associated fibroblasts Nat. Rev. Drug Discov. 2019 18 99 115 10.1038/s41573-018-0004-1 30470818

142. Chen H. Zhao L. Ruan D. Pang Y. Hao B. Dai Y. Wu X. Guo W. Fan C. Wu J. Usefulness of [68Ga]Ga-DOTA-FAPI-04 PET/CT in patients presenting with inconclusive [18F]FDG PET/CT findings Eur. J. Nucl. Med. Mol. Imaging 2021 48 73 86 10.1007/s00259-020-04940-6 32588089

143. Zhao L. Chen S. Chen S. Pang Y. Dai Y. Hu S. Lin L. Fu L. Sun L. Wu H. 68Ga-fibroblast activation protein inhibitor PET/CT on gross tumour volume delineation for radiotherapy planning of oesophageal cancer Radiother. Oncol. 2021 158 55 61 10.1016/j.radonc.2021.02.015 33621587

144. Watabe T. Liu Y. Kaneda-Nakashima K. Shirakami Y. Lindner T. Ooe K. Toyoshima A. Nagata K. Shimosegawa E. Haberkorn U. Theranostics Targeting Fibroblast Activation Protein in the Tumor Stroma: 64 Cu- and 225 Ac-Labeled FAPI-04 in Pancreatic Cancer Xenograft Mouse Models J. Nucl. Med. 2020 61 563 569 10.2967/jnumed.119.233122 31586001 PMC7198371

145. Meyer C. Dahlbom M. Lindner T. Vauclin S. Mona C. Slavik R. Czernin J. Haberkorn U. Calais J. Radiation Dosimetry and Biodistribution of 68 Ga-FAPI-46 PET Imaging in Cancer Patients J. Nucl. Med. 2020 61 1171 1177 10.2967/jnumed.119.236786 31836685 PMC7413240

146. Gerlinger M. Rowan A.J. Horswell S. Larkin J. Endesfelder D. Gronroos E. Martinez P. Matthews N. Stewart A. Tarpey P. Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing N. Engl. J. Med. 2012 366 883 892 10.1056/NEJMoa1113205 22397650 PMC4878653

147. Easwaran H. Tsai H.-C. Baylin S.B. Cancer Epigenetics: Tumor Heterogeneity, Plasticity of Stem-like States, and Drug Resistance Mol. Cell 2014 54 716 727 10.1016/j.molcel.2014.05.015 24905005 PMC4103691

148. Tirkes T. Hollar M.A. Tann M. Kohli M.D. Akisik F. Sandrasegaran K. Response criteria in oncologic imaging: Review of traditional and new criteria Radiographics 2013 33 1323 1341 10.1148/rg.335125214 24025927

149. Borcoman E. Kanjanapan Y. Champiat S. Kato S. Servois V. Kurzrock R. Goel S. Bedard P. Le Tourneau C. Novel patterns of response under immunotherapy Ann. Oncol. 2019 30 385 396 10.1093/annonc/mdz003 30657859

150. Lambin P. Rios-Velazquez E. Leijenaar R. Carvalho S. van Stiphout R.G. Granton P. Zegers C.M. Gillies R. Boellard R. Dekker A. Radiomics: Extracting more information from medical images using advanced feature analysis Eur. J. Cancer 2012 48 441 446 10.1016/j.ejca.2011.11.036 22257792 PMC4533986

151. O’Sullivan F. A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data Biostatistics 2003 4 433 448 10.1093/biostatistics/4.3.433 12925510

152. El Naqa I. Grigsby P.W. Apte A. Kidd E. Donnelly E. Khullar D. Chaudhari S. Yang D. Schmitt M. Laforest R. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes Pattern Recognit. 2009 42 1162 1171 10.1016/j.patcog.2008.08.011 20161266 PMC2701316

153. Pinto dos Santos D. Dietzel M. Baessler B. A decade of radiomics research: Are images really data or just patterns in the noise? Eur. Radiol. 2021 31 1 4 10.1007/s00330-020-07108-w 32767103 PMC7755615

154. Yip S.S.F. Aerts H.J.W.L. Applications and limitations of radiomics Phys. Med. Biol. 2016 61 R150 R166 10.1088/0031-9155/61/13/R150 27269645 PMC4927328

155. Traverso A. Wee L. Dekker A. Gillies R. Repeatability and Reproducibility of Radiomic Features: A Systematic Review Int. J. Radiat. Oncol. Biol. Phys. 2018 102 1143 1158 10.1016/j.ijrobp.2018.05.053 30170872 PMC6690209

156. Zwanenburg A. Vallières M. Abdalah M.A. Aerts H.J.W.L. Andrearczyk V. Apte A. Ashrafinia S. Bakas S. Beukinga R.J. Boellaard R. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping Radiology 2020 295 328 338 10.1148/radiol.2020191145 32154773 PMC7193906

157. Mayo C.S. Kessler M.L. Eisbruch A. Weyburne G. Feng M. Hayman J.A. Jolly S. El Naqa I. Moran J.M. Matuszak M.M. The big data effort in radiation oncology: Data mining or data farming? Adv. Radiat. Oncol. 2016 1 260 271 10.1016/j.adro.2016.10.001 28740896 PMC5514231

158. Orlhac F. Boughdad S. Philippe C. Stalla-Bourdillon H. Nioche C. Champion L. Soussan M. Frouin F. Frouin V. Buvat I. A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET J. Nucl Med. 2018 59 1321 1328 10.2967/jnumed.117.199935 29301932

159. Da-Ano R. Visvikis D. Hatt M. Harmonization strategies for multicenter radiomics investigations Phys. Med. Biol. 2020 65 24TR02 10.1088/1361-6560/aba798 32688357

160. Chalkidou A. O’Doherty M.J. Marsden P.K. False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review PLoS ONE 2015 10 e0124165 10.1371/journal.pone.0124165 25938522 PMC4418696

161. Collins G.S. Reitsma J.B. Altman D.G. Moons K. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD Statement BMC Med. 2015 13 10.1186/s12916-014-0241-z 25563062 PMC4284921

162. Chicco D. Ten quick tips for machine learning in computational biology Biodata Min. 2017 10 35 10.1186/s13040-017-0155-3 29234465 PMC5721660

163. Padhani A.R. Miles K.A. Multiparametric Imaging of Tumor Response to Therapy Radiology 2010 256 348 364 10.1148/radiol.10091760 20656830

164. O’Connor J.P.B. Rose C.J. Waterton J.C. Carano R.A.D. Parker G.J.M. Jackson A. Imaging intratumor heterogeneity: Role in therapy response, resistance, and clinical outcome Clin. Cancer Res. 2015 21 249 257 10.1158/1078-0432.CCR-14-0990 25421725 PMC4688961

165. Vallières M. Freeman C.R. Skamene S.R. El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities Phys. Med. Biol. 2015 60 5471 5496 10.1088/0031-9155/60/14/5471 26119045

166. Mu W. Tunali I. Gray J.E. Qi J. Schabath M.B. Gillies R.J. Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy Eur. J. Nucl. Med. Mol. Imaging 2020 47 1168 1182 10.1007/s00259-019-04625-9 31807885 PMC8663718

167. Delso G. Furst S. Jakoby B. Ladebeck R. Ganter C. Nekolla S.G. Schwaiger M. Ziegler S.I. Performance Measurements of the Siemens mMR Integrated Whole-Body PET/MR Scanner J. Nucl. Med. 2011 52 1914 1922 10.2967/jnumed.111.092726 22080447

168. Besson F.L. Fernandez B. Faure S. Mercier O. Seferian A. Mignard X. Mussot S. Le Pechoux C. Caramella C. Botticella A. 18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: First voxel-wise correlative analysis of human simultaneous [18F] FDG PET-MRI data EJNMMI Res. 2020 10 88 10.1186/s13550-020-00671-9 32734484 PMC7392998

169. Ypsilantis P.-P. Siddique M. Sohn H.-M. Davies A. Cook G. Goh V. Montana G. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks PLoS ONE 2015 10 e0137036 10.1371/journal.pone.0137036 26355298 PMC4565716

170. Wang H. Zhou Z. Li Y. Chen Z. Lu P. Wang W. Liu W. Yu L. Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images EJNMMI Res. 2017 7 11 10.1186/s13550-017-0260-9 28130689 PMC5272853

171. Sibille L. Seifert R. Avramovic N. Vehren T. Spottiswoode B. Zuehlsdorff S. Schäfers M. 18 F-FDG PET/CT Uptake Classification in Lymphoma and Lung Cancer by Using Deep Convolutional Neural Networks Radiology 2020 294 445 452 10.1148/radiol.2019191114 31821122

172. Kawauchi K. Furuya S. Hirata K. Katoh C. Manabe O. Kobayashi K. Watanabe S. Shiga T. A convolutional neural network-based system to classify patients using FDG PET/CT examinations BMC Cancer 2020 20 227 10.1186/s12885-020-6694-x 32183748 PMC7077155

173. Pinochet P. Eude F. Becker S. Shah V. Sibille L. Toledano M.N. Modzelewski R. Vera P. Decazes P. Evaluation of an Automatic Classification Algorithm Using Convolutional Neural Networks in Oncological Positron Emission Tomography Front. Med. 2021 8 628179 10.3389/fmed.2021.628179 33718406 PMC7953145

174. Guo Z. Li X. Huang H. Guo N. Li Q. Deep Learning-Based Image Segmentation on Multimodal Medical Imaging IEEE Trans. Radiat. Plasma Med. Sci. 2019 3 162 169 10.1109/TRPMS.2018.2890359 PMC8553020 34722958