ArticleJournal of hepatocellular carcinoma2025
Predictive Radiomics-Based Model for Recurrence-Free Survival After Curative Resection in Patients with Hepatocellular Carcinoma.
Article in Journal of hepatocellular carcinoma, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Dynamic Vascular Spatiotemporal Heterogeneity on Multiphase CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
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9 authors.
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Abstract
Background: Postoperative recurrence after curative resection is a major concern in the management of hepatocellular carcinoma (HCC). This study aimed to develop a radiomics-based model for predicting recurrence-free survival (RFS) after curative resection. Methods: We retrospectively included 184 patients with early-stage HCC who underwent curative resection. The patients were randomized into training and validation sets in a 7:3 ratio. Radiomics features of the tumors on CT images were extracted to construct the Rad-score. We incorporated the Rad-score, clinical characteristics and biochemical parameters into univariate and multivariate analyses to construct a COX proportional hazards model. A radiomics-based nomogram model for predicting recurrence risk was developed by integrating multiple factors that affect recurrence. Calibration curve was used to assess the predictive performance of the model. Results: Rad-score was constructed using 15 radiomic features. The results of multivariate analyses showed that Rad-score, lactate dehydrogenase (LDH) and alpha-fetoprotein (AFP) were independent predictors of RFS. They categorized patients into different recurrence risk groups, and RFS was significantly prolonged in patients in the low-risk group in the training (p<0.001) and validation sets (p<0.001). The Rad-score based composite prediction model showed good predictive performance with AUC of 0.765 and 0.920 for predicting 3 years RFS in the training and validation sets, respectively. The calibration curves indicated that the nomogram model had a favorable predictive performance. Conclusion: This postoperative predictive model allows for better screening of patients at a high risk of recurrence and is a valuable instrument to guide clinicians in clinical treatment decisions.
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