Machine learning for prognostic impact in elderly unresectable hepatocellular carcinoma undergoing radiotherapy

The StepCox + GBM model could help optimize the use of radiotherapy for elderly HCC patients, improving survival outcomes and guiding personalized treatment strategies.

Authors

Publication Details

Journal
Frontiers in Oncology
Published
2025-04-16
DOI
https://doi.org/10.3389/fonc.2025.1585125
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36
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article

Machine learning for prognostic impact in elderly unresectable hepatocellular carcinoma undergoing radiotherapy

Xian‐Guo Liu, Yuxin Shi
1 citations
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
1.36
article

Machine learning for prognostic impact in elderly unresectable hepatocellular carcinoma undergoing radiotherapy

Xian‐Guo Liu, Yuxin Shi
article en
1 citations

Abstract

The StepCox + GBM model could help optimize the use of radiotherapy for elderly HCC patients, improving survival outcomes and guiding personalized treatment strategies.

Frontiers in OncologyVol. 15
Openalex Percentile: Top 21%
Radiomics and Machine Learning in Medical Imaging
1.36
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Machine learning for prognostic impact in elderly unresectable hepatocellular carcinoma undergoing radiotherapy — Xian‐Guo Liu, Yuxin Shi · Frontiers in Oncology (2025) | TGRS Research Map | TGRS