Combining serum biomarkers and MRI radiomics to predict treatment outcome after thermal ablation in hepatocellular carcinoma

The integrated clinical-radiological-radiomics model showed excellent predictive performance for the prognosis of HCC patients undergoing RFA, surpassing individual models. Key predictors included serum AFP, AFP-L3 levels, intratumoral hemorrhage, and peritumoral low signal intensity. This multimodal approach offers a promising tool for individualized prognostic assessment and improved clinical decision-making.

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Publication Details

Journal
American Journal of Translational Research
Published
2025-01-01
DOI
https://doi.org/10.62347/tfrf1430
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
2.72
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article

Combining serum biomarkers and MRI radiomics to predict treatment outcome after thermal ablation in hepatocellular carcinoma

Ludong Zhao
2 citations
American Journal of Translational Research
Radiomics and Machine Learning in Medical Imaging
2.72
article

Combining serum biomarkers and MRI radiomics to predict treatment outcome after thermal ablation in hepatocellular carcinoma

Ludong Zhao
article en
2 citations

Abstract

The integrated clinical-radiological-radiomics model showed excellent predictive performance for the prognosis of HCC patients undergoing RFA, surpassing individual models. Key predictors included serum AFP, AFP-L3 levels, intratumoral hemorrhage, and peritumoral low signal intensity. This multimodal approach offers a promising tool for individualized prognostic assessment and improved clinical decision-making.

American Journal of Translational ResearchVol. 17(3)
Linyi People's Hospital (CN), Jinzhou Medical University (CN)
Good health and well-being
Openalex Percentile: Top 12%
Radiomics and Machine Learning in Medical Imaging
2.72
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