Multi-parametric MRI Habitat Radiomics Based on Interpretable Machine Learning for Preoperative Assessment of Microsatellite Instability in Rectal Cancer.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu 233000, China (Y.W., B.X., K.W., W.Z., Y.M.); Graduate School of Bengbu Medical University, Bengbu 233000, China (Y.W., B.X., K.W., W.Z.).
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200126, China (A.L., Z.X.).
- MR Research Collaboration Team, Diagnostic Imaging, Siemens Healthineers Ltd, Shanghai 200126, China (M.L.).
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu 233000, China (Y.W., B.X., K.W., W.Z., Y.M.). Electronic address: [email protected].
Abstract
This study constructed an interpretable machine learning model based on multi-parameter MRI sub-region habitat radiomics and clinicopathological features, aiming to preoperatively evaluate the microsatellite instability (MSI) status of rectal cancer (RC) patients. This retrospective study recruited 291 rectal cancer patients with pathologically confirmed MSI status and randomly divided them into a training cohort and a testing cohort at a ratio of 8:2. First, the K-means method was used for cluster analysis of tumor voxels, and sub-region radiomics features and classical radiomics features were respectively extracted from multi-parameter MRI sequences. Then, the synthetic minority over-sampling technique method was used to balance the sample size, and finally, the features were screened. Prediction models were established using logistic regression based on clinicopathological variables, classical radiomics features, and MSI-related sub-region radiomics features, and the contribution of each feature to the model decision was quantified by the Shapley-Additive-Explanations (SHAP) algorithm. The area under the curve (AUC) of the sub-region radiomics model in the training and testing groups was 0.848 and 0.8, respectively, both better than that of the classical radiomics and clinical models. The combined model performed the best, with AUCs of 0.908 and 0.863 in the training and testing groups, respectively. We developed and validated a robust combined model that integrates clinical variables, classical radiomics features, and sub-region radiomics features to accurately determine the MSI status of RC patients. We visualized the prediction process using SHAP, enabling more effective personalized treatment plans and ultimately improving RC patient survival rates.