Development and Validation of an Interpretable Machine Learning Model Based on Gd-EOB-DTPA-enhanced MRI for
Longshan Yang1, Xiaojun Zeng1, Zhelin Lv2
1Department of Hepatobiliary Surgery, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Engineering Research Center for Digital and Intelligent Minimally Invasive Diagnosis and Treatment Equipment Innovation and Application, Guangzhou, China.
Rationale And Objectives:
This study aims to develop and externally validate an interpretable machine-learning model that integrates gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid magnetic resonance imaging (MRI) radiomics, qualitative MRI features, and clinical data to accurately distinguish small (≤2 cm) hepatocellular carcinomas from benign hepatic lesions.
Materials And Methods:
This retrospective, multicenter cohort included 296 lesions (Center A: 209 [146 training, 63 internal test]; Centers B + C: 87 external validation). Three feature categories were extracted and integrated. A multistep feature selection and model tuning pipeline was performed exclusively within the training set using nested cross-validation to prevent data leakage. Seven machine learning (ML) algorithms were compared. In the external validation set, a reader study was performed to compare the diagnostic performance of the fusion model against that of radiologists. SHapley Additive exPlanations (SHAP) was employed to interpret the optimal model.
Results:
The fusion model combining three feature categories achieved the best diagnostic performance. In the internal test set, the fusion model yielded an area under the receiver operating characteristic curve (AUC) of 0.913, compared to 0.791 for the radiomics-only model. Its discriminative ability remained robust in an independent external validation cohort (AUC = 0.824), and remained stable after mitigating interscanner variability with ComBat harmonization (AUC = 0.806). The fusion model showed numerically higher performance than junior radiologists and performed comparably to senior radiologists. SHAP analysis identified hepatitis B virus status, alpha-fetoprotein, and specific hepatobiliary phase-derived radiomics features as key predictors.
Conclusion:
An interpretable ML fusion model demonstrates robust discriminative ability for evaluating small HCCs. Although its numerical advantages over the radiomics-only model and junior radiologists did not reach statistical significance because of the limited external sample size, this noninvasive tool holds significant potential as a reliable diagnostic adjunct with future dynamic threshold optimization.