Mapping the research trends and hotspots on solid pulmonary nodules from 2000-2024: insights from bibliometric analysis.

Highlight box

Key findings

• This study utilized tools such as CiteSpace and Bibliometrix to conduct a bibliometric analysis of the research trends of 1,413 solid pulmonary nodules (PNs) from 2000 to 2024. It sorted out information on publications, authors, etc., identified the core participants in lung cancer research, and explored emerging themes.

What is known and what is new?

• As is known to all, one of the most significant challenges in early lung cancer detection is the accurate and timely identification of PNs.

• This study is the first to conduct a bibliometric analysis of literature related to solid PNs, presenting the research focus, proposing emerging themes such as molecular typing, emphasizing the important role of artificial intelligence (AI) and others in the management of solid PNs, and providing references for subsequent research.

What is the implication, and what should change now?

• This study highlights the importance of interdisciplinary and international cooperation in lung cancer research and treatment. To promote the development of this field, it is necessary to strengthen the application of AI, improve diagnostic accuracy, and optimize management strategies.

Introduction

The incidence and mortality rates of lung cancer are the highest among malignant tumors, making it the leading cause of cancer-related deaths ( 1 ). Pulmonary nodules (PNs) are early-stage lung cancers with potential for cure ( 2 ). Depending on their density, PNs are classified as solid nodules (SNs) and subsolid nodules [SSNs, including pure ground-glass nodules (pGGNs) and part-solid nodules (PSNs)]. Ground-glass nodules (GGNs) are associated with a favorable prognosis, while SNs are reported to have poor prognoses with higher malignancies and more frequent lymph node (LN) metastases ( 3 , 4 ). Research has shown that SNs have a shorter doubling time, higher malignancy, earlier metastasis, and poorer prognosis compared to SSNs when malignant transformation occurs ( 5 ). Therefore, accurate differentiation of the benign and malignant nature of SNs in the early stages is crucial.

Computed tomography (CT) is a common tool for lung cancer screening, significantly improving the detection rate of PNs ( 6 ). However, in clinical practice, SNs appear as circular or oval-shaped areas of increased density on CT scans, often obscuring the blood vessels and bronchi within them. Moreover, SNs share overlapping features with lung cancer on CT images, making it challenging to distinguish their benign or malignant nature ( 7 ). The incidental detection of solid PNs, particularly those measuring less than 6 mm, presents a significant challenge within the workflow of radiologists. This challenge is primarily attributed to the increased workload associated with the identification, comparison, and documentation of these small nodules. Given that lung cancers frequently manifest as nodules, early detection and accurate classification of PNs are essential for reducing morbidity and mortality associated with lung cancer. Recently, there has been a significant focus on accurately predicting the nature of SNs and determining appropriate treatment methods, leading to a wealth of research reports and publications in this area.

For researchers who strive to stay abreast of the most recent trends and progress in this field, the intricacy and vast quantity of the literature can be daunting. Traditional reviews are summaries and critiques of literature related to a specific topic, aiming to synthesize and analyze existing studies. Systematic reviews integrate research findings on a specific topic comprehensively through clearly defined research questions, rigorous literature screening, and systematic evaluation. Both of the two methods may struggle to effectively summarize the research trends and current status of various different subfields from numerous published papers, failing to provide a comprehensive overview of the entire research field or identify important future research directions ( 8 ). Therefore, there is an urgent need for an analysis method. This method should be able to sum up key achievements and milestones and also find out current research focuses and possible future directions.

Bibliometric analysis, as a mathematical and statistical method, processes detailed information from references such as journals, authors, and institutions to generate organized knowledge structures. It provides an objective overview of research prospects, emerging trends, and hot topics in a specific research area, helping researchers understand the progress in their field and guiding them in determining their research paths ( 9 ). To date, bibliometric analysis has been widely used to analyze literature on lung cancer and PNs. However, comprehensive overviews in this field are scarce. Therefore, this paper aims to summarize the basics and developments of solid PNs through bibliometric analysis based on publications retrieved from the Web of Science Core Collection (WoSCC). It elaborates on the current publication landscape, emerging trends, shifts in research frontiers, and qualitative milestones, utilizing various scientific mapping tools to pinpoint identify current challenges and future research directions. We present this article in accordance with the BIBLIO reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1669/rc ).

Methods

Data searching

All relevant original articles on solid PNs were retrieved from the WoSCC and the detailed search strategies are attached in the supplementary material ( Appendix 1 ). Generally, the literature searching was conducted in May, 2024, and the inclusion and exclusion criteria were as follows: (I) time span ranged from January 2000 to May 2024; (II) document type were restricted to original article because it has a broader clinical relevance compared to other types of articles and has presented novel viewpoints; (III) language=English. The searching strategies finally yielded a total of 1,413 documents. WoSCC was chosen as the target database because it is one of the most widely accessed academic databases with more than 12,000 high-quality journals and also has the most comprehensive citation information which is necessary in generating the knowledge networks.

Data collection

Publication metadata, including title, publication year, author details, country, institution, journal, citation number, and full citation profile, were extracted and recorded. The manuscripts focusing on solid PNs were identified based on their titles and abstracts. Two researchers (K.Z. and S.F.C.) independently screened all manuscripts to ensure their relevance to the study topic. The manuscripts were obtained in .txt format and then transferred into scientific mapping tools for further bibliometric analysis.

Statistical analysis

In our study, the bibliometric analysis was primarily accomplished using the most popular bibliometric tools CiteSpace ( 10 ) and Bibliometrix ( 11 ). Generally, the R package Bibliometrix was used for the primary quantitative analysis of scientific outcomes and academic impact of the subject including authors, institutions, journal, countries/regions, and CiteSpace was primarily used for network generation and advanced bibliometric analysis such as burst detection, co-citation network clustering.

Results

Publication literature overview

The number of publications visually reflects the development trends in a specific field within a period. In this study, 1,413 original articles were selected for in-depth bibliometric analysis. As shown in Figure 1 , the trend of number of publications related to solid PNs were relatively stable yearly, with some fluctuations mainly between 2012 and 2018. The average annual number of citations in the field was plotted to determine the significant published literature. The year with both highest publication number and average citation per article was 2018, indicating that the literature during this period significantly influenced the field.

Co-authorship: countries/regions

The co-occurrence and collaboration networks among countries/regions were presented in Figure 2A,2B . Among them, the United States led in publication volume, followed by China. Figure 2B illustrated the collaboration relationship among countries, where all countries in this field showed cooperative relationships. The thickness of the line among countries or regions, the closer their cooperation. The United States has engaged in more joint collaborative research with other countries, including China, Canada, United Kingdom.

Active institutions and authors

The top 10 authors and institutions according to the publication number were summarized in Table 1 and Figure 3A . Among them, Prof. Kurzrock Razelle from the Medical College of Wisconsin stands out as the most prolific author with the highest number of publications and Prof. David S. Hong from University of Texas had the highest number of citations. Figure 3B illustrated the co-occurrence networks of institutions. Interestingly, by analyzing the co-occurrence burst of institutions ( Figure 3C ), we found that several Chinese research institutions showed high burst strength, with Tongji University ranked the first, suggesting that these institutions have made high impact in the most recent years.

Key journals

Based on the number of published articles, the top 10 journals were listed in Table 2 . In Figure 4A , the total number of citations and H-index were included for comprehensively assessing the influence of journals. Most of the top 10 journals specialized in the field of lung diseases as shown in Figure 4B . Among these, Clinical Cancer Research had a significant number of citations and the highest H-index. As illustrated in Figure 4C , the journal of Clinical Cancer Research and Cancer Chemotherapy and Pharmacology published more papers than other journals in this field. In addition, Figure 4D depicts a double overlay map of journals related to artificial intelligence (AI) research on lung disease. The left-hand map displayed the citing literature, mainly distributed across (I) mathematics, systems, mathematical; (II) molecular, biology, immunology; (III) medicine, medical, clinical. The map on the right highlighted the cited literature, predominately located in the (IV) molecular, biology, genetics; (V) health, nursing, medicine, suggesting that these areas serve as the fundamental components.

Keywords analysis

By analyzing the frequency of author keywords over the past two decades, we were able to identify key concepts through co-occurrence analysis. As shown in Figure 5A , these concepts include open label, management, immunotherapy, non-small cell lung cancer (NSCLC), classification, diagnosis, nomogram, risk, radiomics, etc., which are believed to be the current research hot spots in the field of solid PNs. Also, a word cloud view of keywords plus was shown in Figure 5B , where the size of the keyword was proportional to the frequency.

Co-citation references

Table 3 listed the top 10 most cited publications on lung cancer and solid PNs, with Prado et al. [2008] ( 12 ) leading in citations [2,266]. Notably, Topalian et al. ( 13 ) identified nivolumab promotes the remission and survival of advanced melanoma patients through the blocking mechanism of programmed death-1 (PD-1), which provides a basis for clinical treatment of lung cancer patients. Drilon et al. ( 14 ) concluded that larotrectinib had marked and durable antitumor activity in patients with tropomyosin receptor kinases (TRKs) fusion-positive cancer, regardless of the age of the patient or of the tumor type. Most of the high-cited papers shown in Table 3 offered referential treatment methods for pure solid lung cancer caused by SNs. In addition, to fully characterize the research front of solid PNs, we also analyzed the documents in the most recent 5 years [2020–2024] and summarized the most influential references in the last 5 years. References were ranked in sigma and centrality, which was a scientometric indicator of academic novelty and a measure of nodal significance in the network, respectively ( Tables 4,5 ).

Discussion

Research trend of solitary PNs

Using bibliometric methods, we summarized the research progress on SN from 2000 to 2024. Through a comprehensive visualization and analysis using four complementary scientific mapping tools, we qualitatively and quantitatively depicted the knowledge structure, hot research topics, and emerging research frontiers in the field of SN research which is shown in Figure 6A . Since the 1980s and 1990s, there has been a growing interest in SN. However, this interest has been hindered due to the perceived complexity and high uncertainty of the diagnostic process for SN, as well as the cautious approach towards the risks of overdiagnosis and invasive examinations. Recent evidence suggests an increasing number of asymptomatic patients are found to have SN, which is closely associated with early-stage lung cancer. The formation of new SN is also linked to a higher risk of lung cancer, highlighting the importance of early identification of the benign or malignant nature of SN and effective management strategies. However, there isn’t a comprehensive bibliographic analysis available on landmark publications, advancements in different subfields, and emerging research topics. Our objective is to classify the attributes of relevant studies. By discussing clustering labels and the outcomes of burst detection, we aim to forecast and drive forward future research.

Although there has been an overall increase in scientific output related to SN from 2000 to 2024, the publication quantity was relatively stable in the first 12 years, indicating a recent growth in interest. Additionally, we identified that the year with the highest number of publications was 2018, in the same year the average total citations per article per year reached the peak. This indicates that the literature during this period significantly influenced the field. As illustrated in Figure 6B , we specifically identified reference literature with a sharp increase in citation frequency within specific time periods by using burst detection. The results indicated that some articles before 2017 had a significant impact on the subsequent development of SN research. Similarly, based on burst detection, we also found some studies published in 2009, 2015 and 2017 with burst values >10. These studies are now considered pioneering research in the field of SN research.

Frontiers of SN research

Overview

In bibliometric terms, the research frontiers of a particular field refer to a set of cited articles, with the knowledge base composed of references that are commonly cited in Tables 4,5 ( 15 ). By analyzing the word profiles extracted from articles citing a set of reference documents, and subsequently generating co-citation cluster labels. These labels can be used to clarify and explain the main concepts of research frontiers. While burst detection is a method to detect emerging terms independently of the number of citations to the main articles. In our study, key concepts derived from co-citation analysis include LN metastasis (LNM), benign lesions, advanced cancer, occupational exposure, molecular profile analysis, lung adenocarcinoma (LUAD), brain metastasis, which is displayed in Figure 7 . By combining these concepts with burst detection, we are able to identify and clarify research frontiers or emerging concepts at both structural and temporal levels. Burst detection based on keyword co-occurrence has generated keywords such as open label, management, immunotherapy, NSCLC, classification, diagnosis, nomogram, risk, radiomics, and others ( Figure 5A ). Surprisingly, we found that some of the research topics identified through burst detection corresponded to search results on ClinicalTrials.gov. Additionally, we conducted burst detection on citations of the most recent publications [2000–2024] to identify literature that has attracted the attention of peer scholars. Here, we will briefly discuss how emerging research frontiers are driving the field towards promising new directions and explore potential fruitful future avenues.

Deep learning

In recent years, with the improvement of computing power and the availability of large image databases, the application of deep learning to CT preprocessing exceeds the current tumor-node-metastasis (TNM) classification system and provides meaningful prognostic information for the recognition and diagnosis of lung nodules and the classification and staging of lung cancer ( 16 ). Lian et al. included 1,705 cases of NSCLC and proposed a graph convolutional networks (GCNs) model for lung segmentation and survival prediction in NSCLC patients using GCN and CT data. They compared the GCN model with traditional clinical models based on TNM staging and common imaging features and found that the GCN model had the highest area under the curve (AUC) score of 0.732 [95% confidence interval (CI): 0.643–0.821; P<0.0001] ( 17 ). Hendrix et al. developed a deep learn-based AI system and evaluated its sensitivity to detect actionable benign nodules (requiring follow-up), primary lung cancer, and lung metastases. They found that in an external test set (100 scans from 100 patients), the system had a detection sensitivity of 94.3% (82/87, 95% CI: 88.1–98.8%) for benign nodules and 96.9% (31/32, 95% CI: 91.7–100%), with a detection sensitivity of 92.0% (104/113, 95% CI: 88.5–95.5%), comparable to or higher than that of radiologists ( 18 ). Wang et al. introduced DeepLN, a deep convolutional neural network, for the evaluation of lung nodules, and found that DeepLN can identify imaging morphological features, such as burrs, lobules, or pleural indentation, and distinguish between malignant tumors [such as LUAD, lung squamous cell carcinoma (LUSC)] and benign lesions (such as granuloma, inflammation). All classification processes performed by DeepLN are based on chest CT scanning, which provides a non-invasive and repeatable solution for determining the histological subtype of PNs ( 19 ). Although deep learning has great potential in treating lung cancer, it is important to recognize that the benefits of deep learning come at the cost of a slightly higher false positive (FP) rate (average difference of 0.6 FP/s). In addition, addressing issues related to data quality, model interpretability, and ethical considerations is critical to ensuring the successful integration of deep learning into clinical practice. By overcoming these obstacles, we can unlock the full potential of deep learning, paving the way for more effective and personalized lung cancer treatments.

Molecular profile analysis

The application of molecular spectrum analysis at the molecular level has revealed various key biomarkers of lung cancer, which can serve as targets for targeted therapy of lung cancer. Cheng et al. found that ultrashort single-stranded cell-free DNA (uscfDNA) is a new cell-free DNA (cfDNA) biomarker class with characteristics distinct from mononucleosomal cell-free DNA (mncfDNA) in NSCLC patients. Nonetheless, it must be emphasized that to establish specific signal profiles or thresholds, a sufficiently sized cohort with case controls will be needed to reach appropriate statistical power ( 20 ). Zu et al. identified miR-660-5p as a key driver of NSCLC progression and distant metastasis, possibly targeting LIMCH1, SMARCA5, and TPP2 ( 21 ). Singh et al. conducted a study analyzing several miRNAs that have been identified for the treatment of lung cancer, including mir-Let7, mir-34a, mir-134, mir-16-1, mir-320a, mir-148a, mir-125a-5p, mir-497, mir-29, mir-133a, and mir-29a-3p, which target various signaling pathways involved in the pathogenesis of lung cancer ( 22 ). Maharaj et al. found that mutations in the PIK3CA, PTCH1, FGF6, and NRAS genes significantly increased the incidence of venous thromboembolism (VTE), with PIK3CA being the most common ( 23 ). These mutations and related pathways may provide new insights for risk stratification, prevention, and targeted therapy for lung cancer.

Brain metastases (BMs)

In recent years, the incidence of BMs from solid tumors has been sharply increasing. Among these, lung cancer poses the highest risk of brain metastasis, with NSCLC even being able to present with BMs without a known primary tumor (referred to as “brain metastases of unknown primary origin”). For decades, whole-brain radiotherapy (WBRT) has been used as a standard treatment to alleviate symptoms in most patients and extend their survival by several months. However, a prospective study by Zhu et al. demonstrated that the combination of anlotinib with WBRT is more effective in treating patients with multiple resistant BMs compared to WBRT alone, with manageable adverse events ( 24 ). Zhang et al. comparatively analyzed and found that WBRT + simultaneous integrated boost (SIB) improved intracranial local tumor control (LTC) and progression-free survival (PFS), particularly in the subgroup of patients with lung cancer. Additionally, WBRT + SIB also improved long-term control of intracranial tumors in patients with small cell lung cancer. It is worth noting that in toxicity assessment, WBRT + SIB may slightly increase the risk of radiation-induced brain injury, with the risk increasing with dose escalation ( 25 ). Liao et al. conducted a retrospective analysis and then found that the median overall survival (OS) and the median PFS for WBRT alone both shorter than for WBRT plus anti-PD-1 therapy. While both univariate and multivariate analyses indicated that the treatment group (WBRT plus anti-PD-1) was a significant predictor of OS (P=0.040) ( 26 ). Therefore, research on lung cancer BMs should focus on personalized treatment strategies (such as surgery, radiotherapy, chemotherapy, and targeted therapy) in order to improve patients’ quality of life and prognosis.

Tumor size

In clinical practice, tumor size is often used to assess metastasis rates, guide treatment selection, and make other clinical decisions. Siwachat et al. found that tumors larger than 4 cm were predictive factors for nodal micro-metastasis (NMM), with a risk ratio of 6.69 (95% CI: 2.38–18.85, P<0.001) ( 27 ). Choi et al. revealed that as tumor size increased, there was no difference in LNM rates in the mixed GGN (mGGN) group. However, in the pure-solid nodule (PSN) group, there was a statistically significant difference in LNM rates based on tumor size ( 28 ). Kim et al. found that in the subgroup analysis of stage IB NSCLC patients with tumor sizes of 1–3 cm, adjuvant chemotherapy was associated with improved recurrence-free survival (RFS) and OS, even in visceral pleural invasion (VPI) patients with other risk factors ( 29 ). Saijo et al. found a negative correlation between tumor diameter and treatment duration in patients receiving anti-PD-1 antibody therapy. Immunohistochemical staining showed significantly higher Treg cell infiltration in large NSCLC tumors compared to small NSCLC tumors ( 30 ). Bertolaccini et al. thoroughly reviewed data on lobectomy and sublobar resection for early-stage NSCLC and concluded that for tumors smaller than 2 cm, sublobar resection could be a feasible alternative to lobectomy, but precise patient selection and ongoing technological advancements are needed to optimize outcomes ( 31 ). Given the dynamic nature of treatment modalities for NSCLC, healthcare providers must stay abreast of the latest research findings and clinical guidelines. In the process of making treatment decisions, the active participation of patients and the clinical experience of physicians are crucial. Although subjectivity is inevitably present, objective criteria such as tumor size can be fully utilized as important guiding principles to optimize patient care ( 31 ).

Response Evaluation Criteria in Solid Tumors (RECIST)

The RECIST guidelines were initially developed and refined to assess tumor response to cytotoxic drugs (RECIST version 1.1), assuming that tumor growth or the appearance of new lesions indicates disease progression. However, the response patterns to immune checkpoint inhibitors (ICIs) therapy differ from cytotoxic drugs, with approximately 30% of patients experiencing unexpected tumor growth acceleration, known as hyperprogressive disease (HPD) ( 32 ). After neoadjuvant immunotherapy combined with chemotherapy, Xu et al. performed curative resection on resectable tumors evaluated by RECIST in 59 eligible patients and found that there was no correlation between the final pathological results and RECIST evaluation (P=0.086) ( 33 ). In a retrospective study, Choudhury et al. revealed that there is a need to improve RECIST to incorporate the unusual progression patterns observed in ICI therapy ( 34 ). Léger et al. , through systematic analysis, introduced the immunotherapy RECIST (iRECIST) to consider atypical progression patterns, especially pseudo-progression, by dividing the “progressive disease” section into two subclasses: unconfirmed progressive disease (iUPD) and confirmed progressive disease (iCPD). Things previously classified as progressive disease (PD) are now classified as iUPD. To lead to iCPD, further progression in the sum of diameters or new lesions must be recorded in subsequent scans. Conversely, immune complete response (iCR), immune partial response (iPR), or stable disease (SD) can be diagnosed in follow-up scans after iUPD. It is important to note that currently, RECIST 1.1 remains the standard for immuno-oncology in clinical settings, while iRECIST is primarily used in research environments and occasionally as an additional endpoint in certain protocols. Although iRECIST attempts to address potential shortcomings of RECIST 1.1 and consider the atypical response patterns observed during ICI therapy, its superiority has not been definitively proven and requires prospective studies to fully establish its benefits ( 35 ).

LNM

The LNM occurrence in lung cancer is a complex pathological process. As the primary lung cancer grows, tumor angiogenesis occurs, and tumor cells invade LNs and metastasize through lymphatic vessels. Accurately assessing LNM is crucial for the staging, treatment, and prognosis of lung cancer. Earlier in the text, tumor size was mentioned as one of the factors predicting LNM. Additionally, a retrospective study by Huang et al. found that age is a risk factor for LNM, and genetic changes may also be associated with LNM ( 36 ). Xie et al. conducted receiver operating characteristic (ROC) analysis which showed that CER40keV, normalized iodine concentration (NIC), and CER70keV had higher diagnostic efficacy for LNM compared to other quantitative parameters (AUC of 0.794, 0.791, and 0.783, respectively). What’s more, multifactor logistic regression analysis indicated that size, λ, and NIC were independent predictors of LNM, with the combination of size, λ, and NIC showing the highest diagnostic efficacy (AUC =0.892) ( 37 ). Zheng et al. based on univariate analysis, four clinical features independently predicting LNM were identified (P<0.05): Rad score, CT-reported LN status, monocyte count, and red cell distribution width (RDW). However, only radiomics score, CT-reported LN status, and RDW were independently associated with LNM based on multivariate analysis ( 38 ). Yang et al. found that older age (P=0.002) and larger tumor diameter (P<0.001) were associated with a higher tendency for LNM. Additionally, in LNM patients, the total CD8 + T cell density was significantly decreased in invasive margin (IM) (P<0.001) and tumor center (TC) (P<0.001), with lower CD8 + T dysplasia cells density in TC (P=0.003) and higher CD8 + T dysplasia cells density in IM (P<0.001) ( 39 ). Several studies have indicated that a nomogram based on fluorine-18 fluorodeoxyglucose positron emission tomography/CT ( 18 F-FDG PET/CT), integrating radiomics features and age, has higher sensitivity for predicting mediastinal LNM compared to prediction models based solely on PET/CT parameters and clinical features. This may aid in individualized treatment for mediastinal LNM in clinical practice ( 36 , 38 ).

Solid lipid nanoparticles (SLNs)

SLNs are colloidal particles composed of solid lipids dispersed in an aqueous solution, with diameters ranging from 50 nanometers to 1,000 nanometers, demonstrating significant potential for drug delivery in lung cancer treatment. Zhou et al. carried out the vitro release experiments which showed that the drug accumulation release rates of transferrin-modified mangiferin-loaded SLNs (TF-MGF-SLNs) reached over 68% and 72% within 6 hours at pH 4.0 and 7.4, respectively. In vivo studies indicated that, depending on surface modifications, TF-MGF-SLNs exhibited altered cellular internalization, allowing more drugs to successfully enter cells ( 40 ). Wang et al. found that phyllanthi tannin fraction loaded SLNs (PTF-SLNs) had stronger anti-tumor effects than phyllanthi tannin fraction (PTF), with more pronounced cytotoxicity and apoptosis effects in vitro ( 41 ). Najib Ullah et al. found that the bioavailability and biodistribution of bedaquiline-loaded SLNs (BQ-SLNs) in lung tumors were higher compared to the bedaquiline (BQ) suspension. Moreover, BQ-SLNs significantly increased the impact of antioxidant enzymes [malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), glutathione (GSH), glutathione reductase (GR)] in the treatment group rats ( 42 ). Abdelrahman et al. conducted permeability studies and found that compared to the gefitinib (GEF) suspension, both SLNs loaded with GEF (GEF-SLNs) and GEF-loaded polyethylene glycosylated SLNs (GEF-P-SLNs) had higher permeability of GEF. Additionally, both GEF-SLNs and GEF-P-SLNs had lower cell toxicity. Apoptosis studies also indicated that compared to pure GEF, GEF-P-SLNs reduced the number of live lung cancer cells ( 43 ). However, SLNs also have drawbacks, such as polymer transitions during storage that may lead to drug expulsion, accidental crystallization, and nonspecific uptake by the mononuclear phagocyte system.

Immunotherapy

The combination of PD-1/programmed death-ligand 1 (PD-L1) inhibitors with other therapies can to some extent improve the survival and quality of life of lung cancer patients. A single-arm meta-analysis conducted by Zhou et al. found that the benefits of using PD-1/PD-L1 inhibitors alone for treating lung cancer are limited. The most beneficial approach is the combination of dual inhibitors with another treatment ( 44 ). Li et al. found that the combination therapy of any two treatments had a greater ability to inhibit tumor growth compared to single therapy, with the triple therapy showing the most significant impact on tumor growth ( 45 ). Chen et al. conducted a single-arm meta-analysis and found that the combination of PD-1/PD-L1 inhibitors with anti-angiogenic drugs as second-line or second-line therapy for advanced NSCLC patients had good efficacy and safety ( 46 ). It is worth noting that PD-1/PD-L1 inhibitor immunotherapy still faces challenges and issues such as immune resistance. In addition, in clinical practice, the common adverse reactions of PD-1/PD-L1 inhibitors include anemia, anorexia, nausea, thrombocytopenia, leukopenia, weakness, diarrhea, and hypothyroidism, among others. Wang et al. found through summarizing relevant literature that PD-1/PD-L1 inhibitors may induce T cell-mediated destructive thyroiditis, which in turn leads to thyroid dysfunction ( 47 ). The occurrence of these adverse reactions may be related to the overactivation of the immune system. Therefore, when using PD-1/PD-L1 inhibitors, it is necessary to closely monitor the thyroid function and other related indicators of patients in order to detect and manage potential adverse reactions in a timely manner, thereby optimizing the treatment outcomes and quality of life of patients.

Limitations

This study is subject to certain limitations. First, the research data were retrieved solely from WoSCC databases, potentially leading to incomplete data and related results. Second, the selected data were published exclusively in English, which may have resulted in the oversight of important non-English literature. Third, due to the varying analysis methods of the software used, some results may contain errors. Finally, bibliometric analysis is a descriptive study that only examines the current research landscape at a specific point in time. However, medical science is continuously evolving, with new articles published daily, making time constraints unavoidable. Therefore, ongoing and improved research is necessary in the future to address these current limitations.

Conclusions

In this study, bibliometric analysis was employed to visualize articles on solid PNs published between 2000 and 2024. This approach enables researchers to gain insights into the current state, emerging frontiers, and hotspots within the field. Overall, the diagnosis and treatment of solid PNs represent a highly promising area of research that has garnered significant interest from numerous researchers in recent years, with a notable increase in the volume of publications, primarily from scholars in China and the United States. Furthermore, the application of AI in the prediction, diagnosis, and minimally invasive treatment of solid lung nodules is receiving increasing attention and is anticipated to become a critical focus in future research.

Supplementary

The article’s supplementary files as

Acknowledgments

None.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Footnotes

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1669/rc

Funding: This work was supported by the National Key R&D Program of China (No. 2020YFA0714002), Chongqing Health Appropriate Technology Promotion Project (No. 2023jstg044), the Key Project of Technological Innovation and Application Development of Chongqing Science and Technology Bureau (No. CSTC2021jscx-ksbN0030), and the Joint Project of Chongqing Health Commission and Science and Technology Bureau (No. 2022ZDXM006).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1669/coif ). The authors have no conflicts of interest to declare.

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