Machine learning-based Radiomics analysis of preoperative functional liver reserve with MRI and CT image

Abstract

Abstract Objective: Comparing indocyanine green retention rate at 15 min (ICG-R15) can accurately evaluate functional liver reserve, we investigated the ability of Gd-EOB-DTPA-enhanced hepatic magnetic resonance imaging (MRI) and contrast-enhanced computed tomography (CT) image in hepatocellular carcinoma (HCC) patients’ radiomics models for evaluation of functional liver reserve. To assist doctors in evaluating hepatic functional reserve in the hospitalthat lacks expensive ICG equipment. Methods: 190 HCC patients in total were retrospectively enrolled and randomly classified into a training dataset (CT: n = 152, MR: n = 90) and a test dataset (CT: n = 38, MR: n =22). Then, radiomics features from MRI and CT images were extracted. The features associated with the ICG-R15 classificationwere picked out. Six machine learning (ML) classifiers were used for the ML-model investigation, and the accuracy (ACC) and area under ROI curve (AUC) of receiver operating characteristic (ROC) with 95% confidence intervals (CI) utilized for ML-model performance evaluation. Results: 107 different radiomics features were extracted from MRI and CT respectively. The features related to ICG-R15 classification were selected. In MRI groups, when ICG-R15=10% was selected as a threshold, classifier LightGBM performed best for its AUC was 0.932 and ACC 0.955. When ICG-R15=20%, classifier LightGBM performed best for its AUC was 0.938 and ACC 0.913. When ICG-R15=30%, classifier XGBoost performed best for its AUC was 0.972 and ACC 0.955. For CT groups, when ICG-R15=10% was selected as a threshold, classifier LightGBM performed best for its AUC was 0.891 and ACC 0.868. When ICG-R15=20%, classifier SVM performed best for its AUC was 0.877 and ACC 0.842. When ICG-R15=30%, classifier LightGBM performed best for its AUC was 0.927 and ACC 0.947. Conclusions:Both the MRI and CT machine learning models are considered valuable noninvasive methods for the evaluation of functional liver reserve. The performance of the MRI model was better than that of the CT model in the assessment of functional liver reserve.