Predictive value of an AI model integrating MRI radiomics and clinical features for early recurrence of hepatocellular carcinoma prior to radiofrequency ablation.

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Abstract

This study developed an AI-driven radiomics model fusing pre-radiofrequency ablation (RFA) multi-sequence MRI features and clinical variables to predict early recurrence of hepatocellular carcinoma (HCC) after RFA. A total of 169 HCC patients who underwent pre-RFA MRI (January 2015-December 2021) were retrospectively enrolled and randomly assigned to a training set (<i>n</i> = 135) and an internal hold-out test set (<i>n</i> = 34) at an 8:2 ratio using stratified sampling (training: 49 recurrent, 86 non-recurrent; test: 12 recurrent, 22 non-recurrent). Radiomics feature selection involved a three-step strategy of variance threshold filtering, SelectKBest, and least absolute shrinkage and selection operator (LASSO) regression. All data preprocessing, feature selection, and model training were performed exclusively within the training set to prevent information leakage. Support vector machine (SVM), logistic regression (LR), and random forest (RF) classifiers were used to construct radiomics-only, clinical-only, and integrated models. Performance was evaluated using ROC curves (AUC as primary metric), calibration curves, Brier scores, Hosmer-Lemeshow test, and decision curve analysis (DCA). The optimal threshold was determined by the Youden index. Alpha-fetoprotein (AFP), platelet count (PLT), and tumor location were identified as independent clinical predictors (all VIF < 5). Of 11,320 extracted features, 61.5% (<i>n</i> = 6,965) achieved ICC > 0.75, and 16 informative radiomics features were ultimately selected (inter-observer Dice = 0.85 ± 0.06). The radiomics-only models showed robust performance (test AUC: SVM = 0.826, LR = 0.830, RF = 0.826), significantly outperforming clinical-only models (test AUC: SVM = 0.688, LR = 0.706, RF = 0.724; <i>P</i> < 0.05). The integrated models achieved further improvement, with the RF-based integrated model demonstrating superior performance (training AUC = 0.975, test AUC = 0.909; <i>P</i> < 0.0083 after Bonferroni correction). Calibration showed good agreement (Brier score = 0.143, Hosmer-Lemeshow <i>P</i> = 0.367), and DCA demonstrated net clinical benefit across threshold probabilities of 15%-70%. At the optimal threshold (0.42), the RF-based integrated model achieved sensitivity = 83.3%, specificity = 90.9%, PPV = 83.3%, NPV = 90.9%, and accuracy = 88.2%. Preoperative MRI-based radiomics features combined with clinical variables effectively predict early post-RFA HCC recurrence. The integrated radiomics-clinical model outperforms both clinical-only and radiomics-only models, with the RF classifier yielding optimal performance, providing a reliable tool for individualized treatment decision-making.

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