Preoperative Prediction of KRAS Mutation in Rectal Cancer Using a Combined T2-Weighted Imaging Radiomics and Volumetric Apparent Diffusion Coefficient Histogram Model

Purpose To evaluate a combined model incorporating T2-weighted imaging (T2WI)-based radiomics signature and apparent diffusion coefficient (ADC) histogram features for predicting kirsten rat sarcoma virus oncogene (KRAS) mutation status in rectal cancer patients. Methods 220 patients with pathologically confirmed rectal adenocarcinoma from Center I (training dataset: n = 154; internal validation dataset: n = 66) and 61 from Center II (external validation dataset) were retrospectively included. A total of 851 radiomic features from T2WI and 20 ADC histogram features from diffusion-weighted imaging (DWI) were extracted. These two sets of features underwent separate feature selection and were then combined to construct a classification model for KRAS prediction. Model performance was evaluated using ROC curve analysis, and AUCs were compared using the DeLong test. P  < 0.05 was considered statistically significant. Results Four T2WI radiomics features and two ADC histogram features were selected to construct the combined model, which achieved the highest performance with an AUC of 0.823 [95% confidence interval (CI): 0.701–0.931] in the internal validation dataset, outperforming the radiomics-only (AUC = 0.751 [0.623–0.873]) and ADC-only models (AUC = 0.702 [0.571–0.819]). In the external validation dataset, it maintained superior performance (AUC = 0.759 [0.625–0.870]) and significantly outperformed the radiomics-only (AUC = 0.668 [0.514–0.803], P  < 0.05) and ADC-only models (AUC = 0.464 [0.298–0.626], P  < 0.05). Conclusion The combined model demonstrated robust performance for predicting KRAS mutation status in rectal cancer and holds promise as a noninvasive adjunct to genetic testing in clinical settings.