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We reviewed the effectiveness of radiomics alone or with clinical data and assessed the performance of artificial intelligence (AI) models in predicting oncogene mutation status.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>A PRISMA-compliant literature review for studies predicting oncogene mutation status in NSCLC patients using radiomics was conducted by a multidisciplinary team. Meta-analyses evaluating the performance of AI-based models developed with CT-derived radiomics features alone or combined with clinical data were performed. A meta-regression to analyze the influence of different predictors was also conducted.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 890 studies identified, 124 evaluating models for the prediction of epidermal growth factor-1 (EGFR), anaplastic lymphoma kinase (ALK), and Kirsten rat sarcoma virus (KRAS) mutations were included in the systematic review, of which 51 were meta-analyzed. The AI algorithms\u2019 sensitivity\/false positive rate (FPR) in predicting mutation status using radiomics-based models was 0.754 (95% CI 0.727\u20130.780)\/0.344 (95% CI 0.308\u20130.381) for EGFR, 0.754 (95% CI 0.638\u20130.841)\/0.225 (95% CI 0.163\u20130.302) for ALK and 0.475 (95% CI 0.153\u20130.820)\/0.181 (95% CI 0.054\u20130.461) for KRAS. A meta-analysis of combined models was possible for EGFR mutation, revealing a sensitivity of 0.806 (95% CI 0.777\u20130.833) and a FPR of 0.315 (95% CI 0.270\u20130.364). No statistically significant results were obtained in the meta-regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Radiomics-based models may offer a non-invasive alternative for determining oncogene mutation status in NSCLC. Further research is required to analyze whether clinical data might boost their performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Can imaging-based radiomics and artificial intelligence non-invasively predict oncogene mutation status to improve diagnosis in non-small cell lung cancer (NSCLC)?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Radiomics-based models achieved high performance in predicting mutation status in NSCLC; adding clinical data showed limited improvement in predictive performance.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Radiomics and AI tools offer a non-invasive strategy to support molecular profiling in NSCLC. Validation studies addressing clinical and methodological aspects are essential to ensure their reliability and integration into routine clinical practice.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-11962-x","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T17:25:13Z","timestamp":1757352313000},"page":"2157-2185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8667-4011","authenticated-orcid":false,"given":"Almudena","family":"Fuster-Matanzo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfonso","family":"Pic\u00f3-Peris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuensanta","family":"Bellv\u00eds-Bataller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana","family":"Jimenez-Pastor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glen J.","family":"Weiss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Mart\u00ed-Bonmat\u00ed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"L\u00e1zaro S\u00e1nchez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Bazaga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe L.","family":"Banna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfredo","family":"Addeo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Camps","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis M.","family":"Seijo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1ngel","family":"Alberich-Bayarri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"11962_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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He takes overall responsibility for all aspects of the study, including ethics, consent, data handling and storage, and compliance with Good Research Practice.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare relationships with the following companies: G.J.W. is a former employee of SOTIO Biotech Inc., and reports personal fees from Imaging Endpoints II, MiRanostics Consulting, International Genomics Consortium, Angiex, Immunocore, Kymera, Maverix, Quibim, mbiomics, Accent Therapeutics and SPARC\u2014all outside this submitted work; has ownership interest in MiRanostics Consulting, Moderna, Agenus, Aurinia Pharmaceuticals, and Circulogene\u2014outside the submitted work; and has issued patents\u2014all outside the submitted work. A.B.B. is a shareholder and CEO of Quibim. A.A. reports free from BMS, Astrazeneca, Roche, MSD, Pfizer, Eli Lilly, Astellas, Amgen, Novartis, Merck, speaker bureau for Eli Lilly, Astrazeneca, Amgen and has received a research grant from Astrazeneca. A.F.M., A.P.P., F.B.B., A.J.P., D.B. and G.J.W. report personal fees from Quibim related to this work. L.M.B., A.L.S., C.C. and G.B. declare no conflicts of interest. L.M.S. is a scientific advisor for Serum Detect, Median Technologies, AstraZeneca, and Sabartech and has received funding for research from Median Technologies and Serum as well as honoraria for conferences from Menarini, AstraZeneca, and Roche.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"One of the authors, Alfonso Pic\u00f3-Peris (first co-author), has significant statistical expertise.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was not required for this study because it was based exclusively on previously published data and did not involve the collection or analysis of individual patient data.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was not required because the study used only aggregated data from published studies and did not involve new data collection or intervention on human subjects.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Some study subjects or cohorts have been previously reported in the original publications included in this systematic review and meta-analysis.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}