Interpretable deep learning framework based on contrast-enhanced MRI for predicting histological grade of
Wenjun Hu1, Xiuding Cai2,3, Ying Zhao1
1Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China.
Background:
Histopathological grading is a key prognostic marker for hepatocellular carcinoma (HCC). However, the clinical application of deep learning models (DLMs) for predicting HCC grading from medical imaging is limited by their black-box nature. We aimed to develop an interpretable DLM, interpretable HCC grading network (iHCG-Net), to predict HCC grading preoperatively using multi-phase contrast-enhanced magnetic resonance imaging (CEMRI).
Methods:
This study retrospectively enrolled 370 HCC patients who underwent preoperative CEMRI before curative resection. Based on postoperative pathology, the patients were categorized into high-grade (n=136) and low-grade (n=234) HCC groups. They were then stratified into a training cohort (n=259) and a time-independent validation cohort (n=111). Twenty-three clinical-radiological features were collected for all patients. The iHCG-Net, based on the Concept Bottleneck Model (CBM) framework, first encodes CEMRI images using a DenseNet-121 backbone and then leverages a concept regressor to predict the twenty-three clinical-radiological features for final prediction of HCC histological grade. A feature importance score plot was generated to assess the contribution of each feature to the differential diagnosis. Nine baseline predictive models were developed for comparison. The models were evaluated using receiver operating characteristic (ROC) curve analysis and DeLong's test.
Results:
iHCG-Net demonstrated strong predictive performance for HCC grading, achieving areas under the receiver operating characteristic curve (AUCs) of 0.893 in the training cohort and 0.802 in the validation cohort. The model significantly outperformed conventional models, including the clinical-radiological model (CM), radiomics models (RMs), and a clinical-radiomic combined model (CRM) (AUCs: 0.675-0.778, 0.617-0.723; P<0.05). Furthermore, iHCG-Net exhibited performance comparable to that of the DLM (AUCs: 0.920, 0.774; P>0.05), while providing inherent interpretability and mitigating the risk of overfitting. Feature importance analysis identified intratumoral arteries as the most influential feature for predicting HCC grading, with an importance score of 0.213.
Conclusions:
The iHCG-Net can be a promising interpretable artificial intelligence tool for the preoperative prediction of HCC grading.
