Development and validation of an interpretable MRI-based multimodal fusion model for predicting lymph node metastasis
Yunjun Yang1, Lingyu Zeng2, Cuiling Zhu3,4
1Department of Radiology, The First Peoples Hospital of Foshan (Foshan Hospital Affiliated to Southern University of Science and Technology), School of Medicine, Southern University of Science and Technology, Foshan, China.
Objective:
To develop an interpretable multimodal model that integrates pre-treatment Magnetic resonance imaging (MRI)-based deep learning radiomics (DLR) features with Node-RADS scores to predict Lymph node metastasis (LNM) in locally advanced rectal cancer (LARC) patients after neoadjuvant chemoradiotherapy (nCRT).
Methods:
In total, 190 LARC patients from three independent centers were retrospectively included. Deep learning and radiomics features were derived from T2-weighted and diffusion-weighted imaging sequences before treatment. Lymph nodes were scored by radiologists using the Node-RADS system. The DLR Node-RADS (DLRN) combined model was constructed using the logistic regression classifier, and receiver operating characteristic analysis was employed to evaluate model performance. Furthermore, feature contributions were clarified using the SHapley Additive exPlanations (SHAP) analysis method.
Results:
The DLRN joint model has superior performance in predicting LNM, and it outperforms the individual DLR model and the Node-RADS model in both the training and two external validation sets. For training set, the area under the curve (AUC) were 0.941 for the DLRN model, 0.864 for the DLR model, and 0.852 for the Node-RADS model. In external validation set 1, the AUCs were 0.923, 0.810, and 0.765, respectively; in external validation set 2, they were 0.843, 0.817, and 0.801. SHAP analysis determined that Node-RADS and DL features are among the most critical predictive features.
Conclusion:
The DLRN model based on multiparameter MRI has demonstrated a promising predictive ability for evaluating the LNM of LARC patients after nCRT. The novel interpretable framework enhances the clinical credibility of the model in guiding personalized decisions for rectal cancer.
