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

Journal: The oncologist

PMID: 40110765

Abstract

BACKGROUND: Accurate prediction of early recurrence (ER) is essential to improve the prognosis of patients with hepatocellular carcinoma (HCC) underwent thermal ablation (TA). Therefore, a deep learning model system using longitudinal magnetic resonance imaging (MRI) was developed to predict ER of patients with HCC.

Authors

Qingyang Kong

Bladder Infection and Immunity Group (BIIG), UCL Centre for Kidney & Bladder Health, Division of Medicine, University College London, Royal Free Hospital Campus, London, UK.
Kai Li

Department of Gastroenterology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.

Keywords

Aged Carcinoma, Hepatocellular Deep Learning Female Humans Liver Neoplasms Magnetic Resonance Imaging Male Middle Aged Neoplasm Recurrence, Local Prognosis Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (40110765)

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

Abstract

Authors

Keywords

External Resources

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