Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for...

To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.

This retrospective study involved 286 cancer patients confirmed by histopathology from center 1 (Training set) and 66 patients from center 2 (External test set). Radiomics features were extracted from T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences, whereas DL features were obtained using four models: MobileNet-V3-large, Inception-V3, ResNet50, and VGG16. These DL radiomics (DLR) features were then combined to construct a machine learning model. The Shapley additive interpretation (SHAP) tool was utilized to investigate the interpretability of the model. We evaluated and compared the diagnostic performance of senior and junior radiologists, with and without the aid of the optimal DLR model. Kaplan-Meier survival curve was used to analyze the prognosis of patients.

The DLR model outperforms individual DL models and the radiomics model. The MobileNet-V3-large combination radiomics signature demonstrated the best performance, achieving an AUC of 0.878 on the Training set and 0.752 on the External test set. Compared to the traditional radiomics model, the AUC for the Training set increased by 0.094 and by 0.051 for the External test set. This model facilitated improved diagnostic performance among both junior and senior radiologists. Specifically, the AUC values for junior and senior radiologists increased by 0.162 and 0.232, respectively, on the Training set; and by 0.096 and 0.113, respectively, on the External test set. The DLR model demonstrated strong performance in risk stratification for disease-free survival.

The DLR model developed from multiparametric MRI can effectively distinguish cancer LN metastasis and enhance radiologists' diagnostic performance.