Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation

Abstract

Purpose To develop and compare various machine learning (ML) classifiers that employ radiomics extracted from contrast-enhanced magnetic resonance imaging (CEMRI) for diagnosing pathological differentiation of hepatocellular carcinoma (HCC), and validate the performance of the best model. Methods A total of 251 patients with HCCs (n = 262) were assigned to a training (n = 200) cohort and a validation (n = 62) cohort. A collection of 5502 radiomics signatures were extracted from the CEMRI images for each HCC nodule. To reduce redundancy and dimensionality, Spearman rank correlation, minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) approach were employed. Eight ML classifiers were trained to obtain the best radiomics model. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUC). The radiomics model was integrated with liver imaging reporting and data system (LI-RADS) features to design a combined model. Results The eXtreme Gradient Boosting (XGBoost)-based radiomics model outperformed other ML classifiers in evaluating pHCC, achieving an AUC of 1.00 and accuracy of 1.00 in the training cohort. The LI-RADS model demonstrated an AUC value of 0.77 and 0.82 in the training and validation cohorts. The combined model exhibited best performance in both the training and validation cohorts, with AUCs of 1.00 and 0.86 for evaluating HCC differentiation, respectively. Conclusion CEMRI radiomics integrating LI-RADS features demonstrated excellent performance in evaluating HCC differentiation, suggesting an optimal clinical decision tool for individualized diagnosis of HCC differentiation.

Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score

“…In this study aimed at screening the factors influencing RFS, a multifaceted approach combining machine learning techniques and traditional statistical analysis was employed. At first, we utilized XGBoost, a powerful machine learning algorithm known for its effectiveness in handling complex datasets, to screen for relevant variables affecting RFS ( 16 , 17 ). XGBoost is particularly adept at capturing nonlinear relationships and interactions within the data, making it suitable for identifying intricate patterns that might influence RFS outcomes.…”