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Regional and organ-level responses to local lung irradiation in sheep.

Lung is a dose-limiting organ in radiotherapy. This may limit tumour control when effort is made in planning to limit the likelihood of radiation-induced lung injury (RILI). Understanding the factors that dictate susceptibility to radiation-induced pulmonary fibrosis will aid in the prevention and management of RILI, and may lead to more effective personalized radiotherapy treatment. As the interaction of regional and organ-level responses may shape the chronic consequences of RILI, we sought to characterise both aspects of the response in an ovine model. A defined volume of left pulmonary par

AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study.

BACKGROUND: Neoadjuvant chemoimmunotherapy (NACI) has significantly improved survival in patients with resectable non-small cell lung cancer … e Predictor (LC-NICER), a CT-based Artificial Intelligence (AI) system that integrates longitudinal radiomics (tumor texture), deep learning (microenvironmental context), and habitat imaging (tumor and peritumoral subregional dynamics) to predict pCR by analyzing tumor spatiotemporal heterogeneity. The LC-NICER system consists of two complementary predictiv …

Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis.

BACKGROUND: Artificial intelligence (AI) has emerged as a promising tool for prostate cancer (PCa) risk stratification and outcome prediction. However, current studies often lack multicenter external validation, have limited sample sizes, present significant intermodel variability, and face overfitting concerns. OBJECTIVE: This study aimed to comprehensively evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based AI models in predicting biochemical recurrence (BCR) of PCa. METHODS: Systematic searches were conducted in the PubMed, Embase, Web of Science, and Cochrane Li

Challenges and advancements in the diagnosis of central nervous system tuberculosis.

Central nervous system (CNS) tuberculosis is one of the most severe forms of extrapulmonary TB, often leading to substantial morbidity and mortality owing to its complex clinical presentation and frequent diagnostic delays. Despite global TB control efforts, CNS TB continues to present unique difficulties owing to its nonspecific clinical manifestations, the paucibacillary nature of cerebrospinal fluid (CSF), and the limitations of conventional diagnostic methods. The diagnostic dilemma is further compounded in resource-limited settings where advanced molecular tools and neuroimaging may not b

AI-Guided Inference of Morphodynamic Attractor-like States in Glioblastoma.

Background/Objectives: Glioblastoma (GBM) exhibits heterogeneous, nonlinear invasion patterns that challenge conventional modeling and radiomic prediction. Most deep learning approaches describe the morphology but rarely capture the dynamical stability of tumor evolution. We propose an AI framework that approximates a latent attractor landscape of GBM morphodynamics-stable basins in a continuous manifold that are consistent with reproducible morphologic regimes. Methods: Multimodal MRI scans from BraTS 2020 (n = 494) were standardized and embedded with a 3D autoencoder to obtain 128-D latent r

Dose-painting after multi-block endoscopic endonasal surgery in sinonasal tumours: protocol for the SinocaRT randomised phase II trial.

INTRODUCTION: Sinonasal carcinomas are rare malignancies with an overall poor prognosis. These tumours arise in close proximity to vital structures, critical organs and cranial nerves involved in essential sensory and endocrine functions. Although multimodal treatment strategies combining surgery and radiotherapy (RT) are required to maximise the chances of cure while sparing surrounding tissues and reducing the risk of recurrence, they are associated with substantial cumulative morbidity. Building on high conformality and steep gradients of modern dose-painting RT, multidisciplinarity can be

Validation of Biomechanical Computed Tomography for Fracture Risk Classification in Metastatic Hormone-sensitive Prostate Cancer.

BACKGROUND: Guidelines recommend dual-energy x-ray absorptiometry (DXA) screening to assess fracture risk and benefit from antiresorptive therapy in men with metastatic hormone-sensitive prostate cancer (mHSPC) on androgen deprivation therapy (ADT). However, <30% of eligible patients undergo DXA screening. Biomechanical computed tomography (BCT) is a radiomic technique that measures bone mineral density (BMD) and bone strength from computed tomography (CT) scans. OBJECTIVE: To evaluate the (1) correlations between BCT- and DXA-assessed BMD, and (2) associations between BCT-assessed metrics an

Next-generation imaging in prostate cancer.

BACKGROUND: Early detection of clinically significant prostate cancer (csPCa) remains a major challenge in urologic oncology. Although prost … 8F-DCFPyL' OR 'PSMA-11') OR ('artificial intelligence' OR 'machine learning' OR 'deep learning' OR 'radiomics' OR 'neural network'). Only English-language, peer-reviewed original research articles, systematic reviews, meta-analyses, and major consensus guidelines were included. Case reports, editorials, conference abstracts, and non-peer-reviewed publicatio …

Diffusion-Weighted Imaging in the Musculoskeletal System: Evolving Role in Modern Imaging Practice.

Diffusion-weighted imaging (DWI) has evolved from a niche research sequence into an increasingly valuable adjunct to conventional magnetic r … stsurgical assessment of residual disease, as well as opportunistic detection of venous thrombosis. Radiomics and machine learning approaches remain experimental. Recent technical advances, including reduced field-of-view imaging, multi-shot acquisition and improved fat suppression, have mitigated but not eliminated historical limitations of susceptibility …

Glioblastoma-A Contemporary Overview of Epidemiology, Classification, Pathogenesis, Diagnosis, and Treatment: A Review Article.

Glioblastoma (GBM) is one of the most common and aggressive primary malignant tumors of the central nervous system, accounting for about hal … hniques, such as advanced magnetic resonance imaging (MRI), positron emission tomography (PET), and radiomics and radiogenomics tools, allow for more precise assessment of tumor characteristics, prediction of response to therapy, and monitoring of disease progression. Contemporary molecular techniques, including DNA methylation profiling and NGS, enable in- …

Multi-metric predictors of radiofrequency-treated trigeminal neuralgias.

Evaluation of neurovascular compression-related trigeminal neuralgia (NVC-TN) and its resolution through microvascular decompression are dem … and spinal tract [Lin et al. (Flatness of the Meckel cave may cause primary trigeminal neuralgia: A radiomics-based study. J Headache Pain. 2021;22(1):104)]. In the second intraoperative step, we used both ION and patient's testimonies to confirm the localization of the affected branch, evolving hypoesthesia, pain reduction and monitoring of adverse effects …

Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer Using Routinely Collected Clinical Data: Multicenter Retrospective Machine Learning Model Development and External Validation Study.

BACKGROUND: Preoperative nodal assessment in colorectal cancer (CRC) remains imperfect, and uncertainty regarding pathological lymph node metastasis (LNM) may persist despite conventional imaging. Machine learning using routinely available preoperative information may provide complementary patient-level risk estimates without requiring radiomic feature extraction or molecular testing. OBJECTIVE: This study aimed to develop and externally validate machine learning models for preoperative estimation of pathologically confirmed LNM risk in patients with newly diagnosed CRC undergoing direct radi

Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital.

OBJECTIVES: Radiation therapy for lung cancer requires a gross tumour volume (GTV) to be carefully outlined by a skilled radiation oncologis … ve local subset. METHODS: X-ray computed tomography (CT) series in a public data set called "NSCLC-Radiomics" from The Cancer Imaging Archive was first used to train a DL-based lung GTV segmentation model (Model 1). Its performance was assessed using a different open access data set (Interobserver1) of Dutch subjects plus a private Indian data set from a l …

Versatility of the Novalis system to deliver image-guided stereotactic body radiation therapy (SBRT) for various anatomical sites.

Stereotactic radiosurgery (SRS) and fractionated stereotactic radiotherapy (FSRT) programs to treat brain tumors were implemented when we first acquired the Brainlab Novalis system in 2003. Two years later, we started an extra-cranial stereotactic radio-ablation or more appropriately a stereotactic body radiation therapy (SBRT) program using the Brainlab Novalis image-guided system at The Methodist Hospital in Houston, Texas. We hereby summarize our initial experience with this system in delivering image-guided SBRT to a total of 80 patients during our first year of clinical implementation, fr

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30

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… Radiomics Radiomics is a relatively new branch of machine learning that involves converting medical images containing important information related to tumour features into measurable and quantifiable data [ 55 ]. … Radiomic modelling and machine learning incorporating clinical features and radiomic features from pre-treatment imaging is also effective in predicting vertebral compression fractures 1 year following radiotherapy [ 99 ]. …

Machine Learning Meets Cancer.

The role of machine learning (a part of artificial intelligence-AI) in the diagnosis and treatment of various types of oncology is steadily increasing. It is expected that the use of AI in oncology will speed up both diagnostic and treatment planning processes. This review describes recent applications of machine learning in oncology, including medical image analysis, treatment planning, patient survival prognosis, and the synthesis of drugs at the point of care. The fast and reliable analysis of medical images is of great importance in the case of fast-flowing forms of cancer. The introductio

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… The AUC of the ML-based MRI radiomics was 0.92, indicating improved performance over the clinical characteristics model. A new model that combined clinical characteristics and radiomic features was similar to the model that only used radiomic features. … A radiomics study identified optimal ML methods for the radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma [ 60 ]. …

Proceedings of the International Cancer Imaging Society Meeting and 21st Annual Teaching Course : Boston, MA, USA. 12-14 September 2022

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… Conclusions The resampling voxel size impacted the reproducibility of multi-parameter MRI-based radiomic features in patients with brain metastases. The summary of the robust radiomic features against resampling voxel size may be helpful for radiomic studies of brain metastases. O2. … Syngo.via Frontier CT Radiomics (Siemens Healthcare) was used to obtain radiomics features according to statistical and machine learning approaches. Results Radiomics features were predictive for complete pathological response and cancer subtypes. …

Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19.

BACKGROUND: Direct evaluation of vascular inflammation in patients with COVID-19 would facilitate more efficient trials of new treatments and identify patients at risk of long-term complications who might respond to treatment. We aimed to develop a novel artificial intelligence (AI)-assisted image analysis platform that quantifies cytokine-driven vascular inflammation from routine CT angiograms, and sought to validate its prognostic value in COVID-19. METHODS: For this prospective outcomes validation study, we developed a radiotranscriptomic platform that uses RNA sequencing data from human i

Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clini

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… Radiomics also enhances tumor characterization and prognostication in the spine. … Radiomics has likewise revealed hidden cues in non-neoplastic spine disorders. In infectious spondylitis, MRI radiomic signatures can differentiate etiologies that overlap with imaging. …

Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.

Artificial intelligence (AI) is increasingly advancing precision immunotherapy by integrating high-dimensional biomedical data to support diagnosis, treatment selection, and longitudinal monitoring in both cancer and autoimmune diseases. This review summarizes AI applications in biomarker discovery, prediction of immune checkpoint inhibitor (ICI) response and toxicity, neoantigen prioritization, CAR-T cell optimization, and therapeutic antibody engineering. In oncology, multimodal models combining multi-omics, medical imaging, and clinical variables improve patient stratification and non-invas