A CT-based model integrating deep learning features radiomics and body composition for preoperative prediction of microsatellite instability in colorectal cancer: a multicenter study.

[OBJECTIVES] The precise prediction by MSI plays a key role in the perioperative treatment and prognosis of colorectal cancer (CRC) patients. This study seeks to establish an interpretable deep learning radiomics model using enhanced CT images to improve the preoperative prediction of microsatellite instability (MSI) in CRC.

[METHODS] The retrospective study analyzed 873 CRC patients who received curative surgery at three medical centers. This group was separated into a training cohort (Center 1), an internal validation cohort (Center 1), external validation cohort 1 (Centers 2) and external validation cohort 2 (Centers 3). By processing the pre-operative portal venous phase CT enhanced images, deep learning as well as radiomics features was derived and combined with body composition based-clinical risk factors to develop three models to predict the MSI status, namely the deep learning radiomics model (DLR), the clinical model, and the clinical model combining deep learning and radiomics (CDLR), with the use of the random forest algorithm. Model performance was quantified by the areas under the receiver operating characteristic curves through the DeLong test, while calibration and decision curve analyses (DCA) were applied to estimate the potential clinical benefit of the models. Finally, SHAP (Shapley Additive exPlanations) analysis and Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to illustrate the interpretability and visualizability of the model.

[RESULTS] Compared with the other models, the CDLR model exhibited superior performance achieving area under the curves (AUCs) of 0.882 in training cohorts, 0.768 in internal validation cohorts, and 0.803 and 0.751 in external validation cohorts 1 and 2, respectively. The clinical model yielded AUCs of 0.730, 0.683, 0.670 as well as 0.607 across the corresponding cohorts and the DLR achieved AUCs of 0.841, 0.740, 0.779, and 0.712. DCA indicated that the CDLR model provided the greatest clinical benefit in MSI prediction in the external validation cohorts.

[CONCLUSION] In summary, the interpretable CDLR fusion model based on enhanced CT demonstrates promising potential as a noninvasive pre-screening tool for predicting MSI in CRC, supporting individualized treatment strategies, while further prospective validation is needed before routine clinical adoption.