SYSTEMATIC REVIEW article
Front. Oncol.
Sec. Thoracic Oncology
This article is part of the Research TopicArtificial Intelligence Advancing Lung Cancer Screening and TreatmentView all 12 articles
Quality and Accuracy of Radiomics Models in Predicting KRAS Status in Lung Cancer: A Systematic Review and Meta-Analysis
Provisionally accepted
- 1Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China
- 2Gansu Provincial Hospital, Lanzhou, China
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Introduction This study aimed to systematically evaluate the diagnostic performance of radiomics-based models in predicting KRAS gene mutations in lung cancer and quantitatively analyze the methodological quality and reporting standardization of related studies. Methods Original studies evaluating radiomics models for predicting KRAS mutation status in lung cancer patients were identified through systematic searches of databases including PubMed, Embase, China National Knowledge Infrastructure (CNKI), Web of Science, and the Cochrane Library (from inception to June 2025). The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess diagnostic bias risk, the Radiomics Quality Score (RQS, comprising 16 items with a total score of 36) was employed to quantify methodological quality, and the METRICS (10 criteria, 100-point scale) was applied to evaluate reporting standardization. A single-arm meta-analysis was conducted on 20 eligible studies (total sample size: 4,953 cases) to calculate pooled sensitivity, specificity, and the area under the summary receiver operating characteristic curve (SROC AUC). External validation was performed using validation cohorts from 12 studies. Results The mean RQS score of included studies was 9.86 ± 3.7 (range: 4–15, representing 27.4% ± 10.3% of the maximum score), with a mean METRICS score of 59.95 ± 13.5%. The primary analysis revealed pooled sensitivity of 0.80 (95% CI: 0.76–0.83), specificity of 0.78 (95% CI: 0.75–0.82), and AUC of 0.85 (95% CI: 0.82–0.88). Validation cohort results were consistent: sensitivity 0.79 (95% CI: 0.73–0.84), specificity 0.77 (95% CI: 0.71–0.82), and AUC 0.85 (95% CI: 0.81–0.88). Significant heterogeneity was observed among studies, but meta-regression and subgroup analyses (based on key methodological variables such as modeling algorithms, imaging modalities, RQS scores, and validation methods) confirmed stable results across subgroups, demonstrating clinical applicability. Conclusion Radiomics models exhibit moderate diagnostic performance in predicting KRAS mutations in lung cancer. Future efforts should strictly adhere to relevant guidelines, strengthen model validation, and standardize workflows to enhance the practical value of radiomics in precision oncology.
Keywords: KRAS gene mutation, lung cancer, Cancer, Radiomics, Non-small cell lung cancer, deep learning
Received: 08 Sep 2025; Accepted: 05 Dec 2025.
Copyright: © 2025 Luo, Wang, Lu, Wang, Wang, Dong, Gou and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Pengcheng Dong
Yayuan Yang
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