文献类型：期刊文献

英文题名：Quality and accuracy of radiomics models in predicting KRAS status in lung cancer: a systematic review and meta-analysis

作者：Luo, Xindong[1,2,3];Wang, Ziqiang[2,3];Lu, Di[1,2];Wang, Yaping[2,3];Wang, Wenliang[2,3];Dong, Pengcheng[1,2];Gou, Yunjiu[2,3];Yang, Yayuan[1]

第一作者：Luo, Xindong

通信作者：Dong, PC[1];Yang, YY[1];Dong, PC[2];Gou, YJ[2];Gou, YJ[3]

机构：[1]Chinese Acad Agr Sci, Lanzhou Inst Husb & Pharmaceut Sci, Key Lab Vet Pharmaceut Dev, Minist Agr & Rural Affairs, Lanzhou, Peoples R China;[2]Gansu Univ Chinese Med, Gansu Prov Hosp, Clin Med Coll 1, Lanzhou, Peoples R China;[3]Gansu Prov Hosp, Dept Thorac Surg, Lanzhou, Peoples R China

第一机构：Chinese Acad Agr Sci, Lanzhou Inst Husb & Pharmaceut Sci, Key Lab Vet Pharmaceut Dev, Minist Agr & Rural Affairs, Lanzhou, Peoples R China

通信机构：[1]corresponding author), Chinese Acad Agr Sci, Lanzhou Inst Husb & Pharmaceut Sci, Key Lab Vet Pharmaceut Dev, Minist Agr & Rural Affairs, Lanzhou, Peoples R China;[2]corresponding author), Gansu Univ Chinese Med, Gansu Prov Hosp, Clin Med Coll 1, Lanzhou, Peoples R China;[3]corresponding author), Gansu Prov Hosp, Dept Thorac Surg, Lanzhou, Peoples R China.|[10735]甘肃中医药大学;

年份：2026

卷号：15

外文期刊名：FRONTIERS IN ONCOLOGY

收录：;Scopus(收录号：2-s2.0-105028633073);WOS:【SCI-EXPANDED(收录号:WOS:001667976200001)】；

基金：The author(s) declared that financial support was received for this work and/or its publication. National Key R&D Plan (grant no. 2021YFF0702405), General Program of National Natural Science Foundation of China -Mechanism of Hepcidin-Induced Activation of the CGAS-STING Pathway in Regulating Antioxidant Stress during Adipose Tissue Browning in Plateau Pikas (Project Number: 32470569). Lanzhou Science and Technology Plan Project -Isolation and Identification of Intestinal Probiotics from Plateau Pikas and Their Development and Utilization for the Treatment of Intestinal Inflammation (Project Number: 2023-1-34). Gansu Provincial Science and Technology Program Project (24YFWA005).

语种：英文

外文关键词：KRAS gene mutation; lung cancer; cancer; radiomics; non-small cell lung cancer; deep learning

摘要：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.