Predicting early recurrence of hepatocellular carcinoma after thermal ablation based on longitudinal MRI with a deep learning approach

The PrePost model developed using longitudinal MRI showed outstanding performance for predicting post-ablation ER of HCC. The DL_Clinical model could stratify the patients into high- and low-risk groups, which may help physicians in treatment and surveillance strategy selection in clinical practice.

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Publication Details

Journal
The Oncologist
Published
2025-03-01
DOI
https://doi.org/10.1093/oncolo/oyaf013
Citations
3
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
4.08
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article

Predicting early recurrence of hepatocellular carcinoma after thermal ablation based on longitudinal MRI with a deep learning approach

Kai Li, Qingyang Kong
3 citations
The Oncologist
Radiomics and Machine Learning in Medical Imaging
4.08
article

Predicting early recurrence of hepatocellular carcinoma after thermal ablation based on longitudinal MRI with a deep learning approach

Kai Li, Qingyang Kong
article en
3 citations

Abstract

The PrePost model developed using longitudinal MRI showed outstanding performance for predicting post-ablation ER of HCC. The DL_Clinical model could stratify the patients into high- and low-risk groups, which may help physicians in treatment and surveillance strategy selection in clinical practice.

The OncologistVol. 30(3)
Sun Yat-sen University (CN), Third Affiliated Hospital of Sun Yat-sen University (CN)
Good health and well-being
Openalex Percentile: Top 7%
Radiomics and Machine Learning in Medical Imaging
4.08
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