Systematic Review of Radiomics Applications in Small Cell Lung Cancer: Insights from Bibliometric Analysis and Research Frontiers

Background: Radiomics has shown significant promise in Small Cell Lung Cancer (SCLC) research, yet trends and hotspots remain unclear. This bibliometric analysis identifies future research directions. Method: Publications on radiomics in SCLC (2000-2025) were retrieved from Web of Science Core Collection. Bibliometrix, CiteSpace, and VOSviewer analyzed countries, institutions, authors, journals, keywords, and references. Results: Analysis of 725 articles revealed marked growth over the past decade. China and the USA were the leading contributors. Institutionally, the University of Texas System was the top contributor, while Shanghai Jiao Tong University led in collaborations. Dekker Andre was the most published author. Frontiers in Oncology published the most articles; Magnetic Resonance Imaging was the most cited. Hotspots identified through keyword and co-citation analysis include radiomics, machine learning, feature selection, survival prediction, and tumor microenvironment. Discussion: SCLC has the characteristics of strong invasiveness and poor prognosis. Radiomics uses artificial intelligence for preoperative diagnosis, efficacy assessment, prognosis prediction, and genotyping. Currently facing challenges such as sample scarcity, data heterogeneity, insufficient model generalization, and a lack of clinical translation standards. In the future, we need to focus on multimodal image fusion, deep feature mining for machine learning, gene regulatory network analysis, multi-center verification, and unified clinical standards. This study is limited to WoSCC English data, software analysis bias, and timeliness, and needs to be optimized later. Conclusion: Radiomics enables early SCLC detection through integrative image-feature analysis. AI-assisted imaging diagnosis, personalized treatment, and prognostic prediction hold significant potential to enhance progression prediction accuracy and advance novel therapies.

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Journal
Current Respiratory Medicine Reviews
Published
2025-12-11
DOI
https://doi.org/10.2174/011573398x415236251126075039
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Systematic Review of Radiomics Applications in Small Cell Lung Cancer: Insights from Bibliometric Analysis and Research Frontiers

Y. Zhang, Min Tan, Fajin Lv, Ke Zhang
Current Respiratory Medicine Reviews
Radiomics and Machine Learning in Medical Imaging
article

Systematic Review of Radiomics Applications in Small Cell Lung Cancer: Insights from Bibliometric Analysis and Research Frontiers

Y. Zhang, Min Tan, Fajin Lv, Ke Zhang
article en

Abstract

Background: Radiomics has shown significant promise in Small Cell Lung Cancer (SCLC) research, yet trends and hotspots remain unclear. This bibliometric analysis identifies future research directions. Method: Publications on radiomics in SCLC (2000-2025) were retrieved from Web of Science Core Collection. Bibliometrix, CiteSpace, and VOSviewer analyzed countries, institutions, authors, journals, keywords, and references. Results: Analysis of 725 articles revealed marked growth over the past decade. China and the USA were the leading contributors. Institutionally, the University of Texas System was the top contributor, while Shanghai Jiao Tong University led in collaborations. Dekker Andre was the most published author. Frontiers in Oncology published the most articles; Magnetic Resonance Imaging was the most cited. Hotspots identified through keyword and co-citation analysis include radiomics, machine learning, feature selection, survival prediction, and tumor microenvironment. Discussion: SCLC has the characteristics of strong invasiveness and poor prognosis. Radiomics uses artificial intelligence for preoperative diagnosis, efficacy assessment, prognosis prediction, and genotyping. Currently facing challenges such as sample scarcity, data heterogeneity, insufficient model generalization, and a lack of clinical translation standards. In the future, we need to focus on multimodal image fusion, deep feature mining for machine learning, gene regulatory network analysis, multi-center verification, and unified clinical standards. This study is limited to WoSCC English data, software analysis bias, and timeliness, and needs to be optimized later. Conclusion: Radiomics enables early SCLC detection through integrative image-feature analysis. AI-assisted imaging diagnosis, personalized treatment, and prognostic prediction hold significant potential to enhance progression prediction accuracy and advance novel therapies.

Current Respiratory Medicine ReviewsVol. 22
The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Chongqing Medical University (CN)
Openalex Percentile: Top 45%
Radiomics and Machine Learning in Medical Imaging
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