Interpretable prediction of macrotrabecular-massive HCC and recurrence-free survival by integrating MRI LI-RADS features with deep learning habitat radiomics.

objectiveMacrotrabecular-massive (MTM+) hepatocellular carcinoma (HCC) is associated with poor prognosis and early recurrence. We developed and validated a nomogram integrating magnetic resonance imaging LI-RADS features with deep learning (DL) habitat radiomics for preoperative prediction of MTM + HCC and stratifying patients according to recurrence-free survival (RFS). MATERIALS AND

methodsIn this retrospective multicenter study, 607 patients with early-stage HCC who underwent curative-intent surgical resection (mean age ± standard deviation, 59.6 ± 10.6 years; 474 males, 133 females) were divided into the training (n = 304), internal validation (n = 131), and external (n = 172) test sets. Liver Imaging Reporting and Data System (LI-RADS) features, LI-RADS categorization, and clinical features were analyzed. Finally, a nomogram integrating LI-RADS features, habitat radiomics, and DL features was developed using multivariate logistic regression. Multivariable Cox regression analyses were performed to identify independent prognostic factors.

resultsAt multivariable analysis, habitat radiomics score, DL score, alpha-fetoprotein (AFP), alanine transferase (ALT), and fat mass were independent predictors of MTM + HCC. The DL habitat radiomics nomogram demonstrated powerful performance with areas under the receiver operating characteristic curve (AUCs) of 0.897 (95% confidence interval [CI]: 0.854-0.940), 0.757 (95% CI: 0.674-0.827), and 0.858 (95% CI: 0.797-0.906) in the training, internal validation, and external test sets, respectively. After multivariable analysis, AFP (p < 0.001) and nomogram-predicted MTM state (p = 0.039) resulted as independent prognostic factors.

conclusionThe nomogram integrating LI-RADS features, habitat radiomics, and DL features accurately identified patients with MTM + HCC and stratified patients by RFS. KEY POINTS: Question Preoperative integrative methods for accurate prediction of MTM + HCC and postoperative recurrence risk stratification remain insufficient. Findings The nomogram integrating LI-RADS features and DL habitat radiomics effectively diagnosed MTM + HCC and showed superior diagnostic performance compared to the Clinical-radiologic model. Relevance statement The nomogram provides a robust, non-invasive tool for preoperative identification of MTM + HCC and recurrence risk stratification, facilitating surgical planning and postoperative surveillance strategies.