Histopathologic deep learning model for predicting tumor response to hepatic arterial infusion chemotherapy plus TKIs and ICIs in large hepatocellular carcinoma
Integrating TKIs and ICIs with HAIC significantly improved ORR, PFS, and OS in all stages of large HCCs. HAIM, derived from histopathological images of the biopsy, showed potential clinical aid for predicting HTI response, providing a novel tool for personalized management of HCC.
Authors
- Chun-Yu Lin (ORCID: https://orcid.org/0000-0001-6995-3326)
- Shuai Kang (ORCID: https://orcid.org/0000-0002-4346-0889)
- Yongcheng Ren (ORCID: https://orcid.org/0000-0002-8673-4640)
- Qinghua Cao (ORCID: https://orcid.org/0000-0002-1405-040X)
- Changxuan You (ORCID: https://orcid.org/0000-0001-9348-2149)
- Fang Liu (ORCID: https://orcid.org/0000-0002-5526-4535)
- Shurong Li (ORCID: https://orcid.org/0000-0001-7180-9462)
- Shuqi Li (ORCID: https://orcid.org/0000-0002-8133-6838)
- Yu Huang
- Jing Zhang (ORCID: https://orcid.org/0009-0001-5370-0014)
Institutions
- University of Electronic Science and Technology of China (CN)
- Sun Yat-sen University (CN)
- The Seventh Affiliated Hospital of Sun Yat-sen University (CN)
- Nanfang Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Institute for Advanced Study (DE)
- Southern Medical University (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-06-06
- DOI
- https://doi.org/10.1186/s40644-025-00885-x
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province
- Southern Medical University
- Nanfang Hospital
- Shenzhen Science and Technology Innovation Program