{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T20:24:03Z","timestamp":1787171043960,"version":"build-2736575974"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"1185","license":[{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate a machine learning (ML) model that integrates clinical data and 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG)-PET radiomic features for predicting RAS mutation status and prognosis in patients with colorectal cancer (CRC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 90 patients (mean age, 64\u2009years, 60 men) with CRC who underwent pretreatment 18F-FDG-PET\/CT. Radiomic features were extracted from PET images. Ten clinical variables and 49 radiomic features were analyzed. Using the AutoGluon ML framework, 3 models (clinical, radiomics, and combined) were developed with 10-fold cross-validation. RAS mutation prediction was evaluated by the AUC, and interpretability was assessed using SHAP analysis. Survival outcomes were evaluated using a RSF model, and risk scores for disease progression were calculated based on true RAS and ML-predicted RAS models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The radiomics ML model demonstrated the highest performance with an AUC of 0.675. SHAP analysis identified NGTDM_Complexity, kurtosis, and skewness as key contributors. For survival analysis, the C-indices based on true and ML-predicted RAS status were 0.685 and 0.675, respectively. A strong correlation was observed between the risk scores derived from the 2 models (r\u2009=\u20090.99, \u03c1\u2009=\u20090.98). Kaplan-Meier analysis demonstrated clear separation between high- and low-risk groups for both models (log-rank P &amp;lt; .01), with comparable survival curves.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Although the radiomics-based ML model demonstrated moderate performance in predicting RAS mutation status, it provided prognostic stratification comparable to that based on true genetic profiles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>18F-FDG PET-based radiomics with ML may have potential as a noninvasive approach for genetic profiling and prognostic assessment in CRC, although further validation is required.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjr\/tqag129","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T13:29:28Z","timestamp":1783085368000},"page":"1890-1901","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning models using 18F-FDG PET\/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer"],"prefix":"10.1093","volume":"99","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9224-1730","authenticated-orcid":false,"given":"Masatoyo","family":"Nakajo","sequence":"first","affiliation":[{"name":"Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences , Kagoshima 890-8544,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daisuke","family":"Hirahara","sequence":"additional","affiliation":[{"name":"Department of Management Planning Division, Harada Academy , Kagoshima 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