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ORIGINAL RESEARCH article
Front. Oncol.
Sec. Radiation Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1585125
This article is part of the Research Topic AI-Based Prognosis Prediction and Dose Optimization Strategy in Radiotherapy for Malignant Tumors View all articles
Machine Learning for Prognostic Impact in elderly unresectable Hepatocellular Carcinoma Undergoing Radiotherapy
Provisionally accepted
Xianguo Liu *
Yuhui Shi - AVIC 363 Hospital, Chengdu, China
This study develops a machine learning-based predictive model to evaluate the survival outcomes of elderly patients with unresectable hepatocellular carcinoma (HCC) undergoing radiotherapy.The 2377 patients from SEER database were divided into training and internal validation cohorts. Additionally, 99 patients from our hospital were used for an external validation cohort. In the training cohort, 101 machine learning-based radiomics models were developed, and the optimal model's performance was subsequently evaluated in both the internal and external validation cohorts.The StepCox + GBM model demonstrated the highest C-index of 0.7 in the training cohort. The model was further evaluated using area under the receiver operating characteristic (AUC-ROC) curves, with AUC values 2 ranging from 0.736 to 0.783, indicating strong predictive performance. Furthermore, the calibration curve and decision curves confirmed that the model had good predictive performance.The StepCox + GBM model could help optimize the use of radiotherapy for elderly HCC patients, improving survival outcomes and guiding personalized treatment strategies.
Keywords: Hepatocellular Carcinoma, Radiotherapy, survival prognosis, machine learning, survial
Received: 28 Feb 2025; Accepted: 31 Mar 2025.
Copyright: © 2025 Liu and Shi. 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:
Xianguo Liu, AVIC 363 Hospital, Chengdu, China
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