An Integrated Model Combined Conventional Radiomics and Deep Learning Features to Predict Early Recurrence of Hepatocellular Carcinoma Eligible for Curative Ablation: A Multicenter Cohort Study
The DLRR model noninvasively and efficiently predicts ER after curative ablation in HCC patients, which helps to categorize the risk in patients to formulate precise diagnosis and treatment plans and management strategies for patients and to improve the prognosis.
Authors
- Weidong Jia (ORCID: https://orcid.org/0009-0008-1419-3401)
- Lei Tang (ORCID: https://orcid.org/0009-0006-1948-1643)
- Yu Zhu (ORCID: https://orcid.org/0000-0002-1998-0294)
- Yonghai Li (ORCID: https://orcid.org/0000-0001-8911-0481)
- Guixiang Qian
- Xiangyi Bu
- Xuedi Lei
- Mingtong Wei
Institutions
- University of Science and Technology of China (CN)
- Shandong University (CN)
- Bengbu Medical College (CN)
- Anhui Medical University (CN)
- Wenzhou Medical University (CN)
- Anhui Provincial Hospital (CN)
- Second Affiliated Hospital of Anhui Medical University (CN)
- Hefei First People's Hospital (CN)
Publication Details
- Journal
- Journal of Computer Assisted Tomography
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1097/rct.0000000000001764
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Chinese Academy of Sciences
- Anhui University
- Anhui University of Chinese Medicine
- Institute of Automation, Chinese Academy of Sciences
- Anhui Provincial Key Research and Development Plan