A multimodal interpretable deep learning-radiomics framework for predicting lymph node metastasis following neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter validation study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China.
- Department of Pathology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China. [email protected].
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. [email protected].
Abstract
Accurate assessment of lymph node metastasis (LNM) following neoadjuvant chemoradiotherapy (nCRT) presents a significant clinical challenge and is essential for the management of locally advanced rectal cancer (LARC). In this multicenter study, we developed and externally tested a multimodal MRI-based framework that integrates clinical demographics, handcrafted radiomic signatures, and deep learning (DL)-derived features to predict post-nCRT lymph nodal status. This study enrolled 382 LARC patients who underwent surgery after nCRT at four centers. Post-nCRT T2-weighted (T2WI) and diffusion-weighted (DWI) MRI images were used to extract radiomic and DL-derived features of tumors. After feature harmonization and selection, a predictive model was constructed using a DL fusion network and a random forest algorithm. The model performance was evaluated across the training, validation, internal test, and external test cohorts using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Shapley Additive Explanation (SHAP) and gradient-weighted Class Activation Mapping (Grad-CAM) were used to enhance the model's interpretability. The combined model, which included clinical, radiomics and DL-derived features, demonstrated the optimal predictive capacity, with an AUC of 0.771 in the external test dataset. This approach shows promise for noninvasively determining treatment response, prognosis, and surgical management.