MRI-derived radiomics for risk stratification of tumor deposits in rectal cancer: a dual-center study

To evaluate the diagnostic utility of MRI-based radiomics in stratifying the risk of tumor deposits (TD) in patients with rectal cancer (RC).

This study retrospectively analyzed 729 patients with RC from two institutions (January 2018-August 2024). Patients were classified into three groups according to the number of TD: no TD (TD0), 1-2 TD (TD1-2), and ≥ 3 TD (TD3+). Radiomics features were extracted from the tumor and the largest nodule within the rectal mesentery on MRI images. Predictive models were developed with the XGBoost algorithm. Model performance was evaluated using the receiver operating characteristic curve, area under the curve, confusion matrix, precision, accuracy, recall, and F1 score.

Three hundred seventy-six patients were ultimately included and allocated into training, test, and validation sets. The tumor model (developed using tumor features) achieved AUCs of 0.871 (test set) and 0.848 (validation set), with corresponding accuracy, precision, recall, and F1 of 0.745/0.716, 0.764/0.688, 0.764/0.734, and 0.764/0.710, respectively. The nodule model (developed using the largest nodule) yielded AUCs of 0.839/0.804, accuracy of 0.673/0.637, precision of 0.571/0.614, recall of 0.800/0.686, and F1 of 0.667/0.648 in the test and validation sets, respectively. The fusion model, which combined tumor and nodule features, achieved enhanced performance with AUCs of 0.873/0.858, accuracy of 0.800/0.784, precision of 0.804/0.712, recall of 0.745/0.775, and F1 of 0.774/0.742, outperformed both individual models and two radiologists (accuracy 0.676/0.589).

MRI-derived radiomics demonstrates significant potential for risk stratification of TD in RC.

The radiomics model integrating tumor features and maximal short-axis diameter of mesorectal nodules effectively predicts three distinct quantity-based categories of TD in RC, enabling preoperative risk stratification and assisting personalized treatment planning.

Tumor and nodule features support effective stratification of TD. The fusion model for TD classification outperforms two radiologists. MRI-based radiomics aids TD risk stratification.