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ORIGINAL RESEARCH article

Front. Oncol.

Sec. Cancer Epidemiology and Prevention

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1660569

This article is part of the Research TopicAdvanced Machine Learning Techniques in Cancer Prognosis and ScreeningView all 10 articles

Development of a weighted Alpha-Fetoprotein tumor burden score-integrated nomogram for predicting overall survival in locally ablated hepatocellular carcinoma patients

Provisionally accepted
  • 1Beijing Ditan Hospital, Capital Medical University, Beijing, China
  • 2Beijing You'an Hospital Affiliated to Capital Medical University, Beijing, China

The final, formatted version of the article will be published soon.

Introduction: The Weighted Alpha-Fetoprotein Tumor Burden Score (WATS) shows promise for hepatocellular carcinoma (HCC) prognosis, but its usefulness in local ablation patients is uncertain, and no validated nomograms exist for overall survival (OS) prediction. Methods: This retrospective study enrolled 862 HCC patients who underwent local ablation therapy at Beijing You'an Hospital between January 1, 2015 and December 31, 2022. Participants were randomly allocated into a training cohort (n=603) and validation cohort (n=259) in a 7:3 ratio. Based on the median value of the WATS score, patients were stratified into low-risk (n=431) and high-risk (n=431) groups. The Kaplan-Meier (KM) curve was used to compare the prognosis between the two groups. Potential prognostic factors were screened via least absolute shrinkage and selection operator (Lasso) regression, followed by construction of a WATS-incorporated nomogram prediction model using Cox proportional hazards regression. The SHapley Additive exPlanations (SHAP) method was employed to interpret variable contributions within the model. Model performance was evaluated via Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Patients were stratified into low-and high-risk groups according to the nomogram scores, and KM curves were used to compare OS differences between the two groups. Results: The study identified the WATS, age, history of drinking, and prealbumin as independent prognostic factors for OS, and successfully established a nomogram model for OS prediction. The ROC curves, calibration curves, and DCA all confirmed that the model possesses good discriminative ability, calibration accuracy, and clinical utility. KM curves demonstrated that the nomogram could effectively stratify patients into different risk categories with satisfactory predictive performance. Conclusion: This study developed and validated a novel prognostic nomogram incorporating the WATS to assess OS in HCC patients receiving local ablation therapy. The nomogram demonstrated robust discriminative ability, enabling accurate prediction of 3-, 5-, and 8-year OS rates, thereby providing clinicians with a reliable tool for individualized risk assessment and treatment decision-making.

Keywords: Hepatocellular Carcinoma, Weighted Alpha-Fetoprotein Tumor Burden Score, overall survival, nomogram, Local ablation

Received: 06 Jul 2025; Accepted: 22 Sep 2025.

Copyright: © 2025 Wang, Gu, Qiao, Yuan, Caixia and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Xiaoxue Yuan, yuanxiaoxue1@ccmu.edu.cn
Ronghua Jin, ronghuajin@ccmu.edu.cn

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