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Published on: September 22, 2013
Construction of an interpretable multimodal image model for differentiating T1-stage nasopharyngeal carcinoma from
Kai-Jie Wang1, Tian-Cheng Lin2, Si-Jia Zuo3
1Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou 570311, China; Department of Radiology, Shenzhen Longgang Second People's Hospital, Shenzhen 518112, China.
Background And Objective:
Differentiating T1-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) is challenging. This study aims to construct and validate a multimodal model combining magnetic resonance imaging (MRI) and endoscopy to distinguish T1-NPC from BH. Additionally, SHapley Additive exPlanations (SHAP) are used for model interpretability analysis.
Materials And Methods:
Data from 161 patients with histologically confirmed diagnoses between 2015 and 2022 were retrospectively collected, including 95 cases of T1-NPC and 66 cases of BH. Regions of interest (ROI) were drawn based on MRI and endoscopy to extract features. Feature selection techniques, such as elastic net, recursive feature elimination, and deep learning, were used to identify the optimal feature subset. Naive Bayes, Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), k-Nearest Neighbors (kNN), and Multilayer Perceptron (MLP) were applied to establish the MRI radiomics model and the MRI-endoscopy combined model. SHAP was used to perform interpretability analysis of the models.
Results:
The MRI-endoscopy combined model outperformed the radiomics model, with the MLP-based model showing the best performance. The mean AUC of the test set reached 0.98, with an accuracy of 0.90, precision of 0.90, sensitivity of 0.93, and specificity of 0.86. SHAP analysis revealed that texture features (including GLSZM, GLCM, and GLRLM) and first-order features were critical for distinguishing T1-NPC from BH.
Conclusion:
Compared to traditional radiomics methods, the multimodal model combining MRI and endoscopy more accurately distinguishes between benign and malignant tissues. SHAP enables visualization of feature contributions and model predictions, highlighting the model's clinical potential.
