The British journal of radiology · 2026
Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer.
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Nakajo Masatoyo, Hirahara Daisuke, Baba Kenji, Hirahara Mitsuho, Eizuru Yukari, Tani Atsushi, Takumi Koji, Kamimura Kiyohisa, Kanzaki Fumiko, Ohtsuka Takao, Yoshiura Takashi
Аннотация
OBJECTIVE: 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).
METHODS: This retrospective study included 90 patients (mean age, 64 years, 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.
RESULTS: 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 = 0.99, ρ = 0.98). Kaplan-Meier analysis demonstrated clear separation between high- and low-risk groups for both models (log-rank P < .01), with comparable survival curves.
CONCLUSION: 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.
ADVANCES IN KNOWLEDGE: 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.
METHODS: This retrospective study included 90 patients (mean age, 64 years, 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.
RESULTS: 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 = 0.99, ρ = 0.98). Kaplan-Meier analysis demonstrated clear separation between high- and low-risk groups for both models (log-rank P < .01), with comparable survival curves.
CONCLUSION: 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.
ADVANCES IN KNOWLEDGE: 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.