CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy
An AI-based prognostic model was developed for advanced HCC using multi-national patients. The model extracts spatial-temporal information from CT scans and integrates it with clinical variables to prognosticate. The model demonstrated superior prognostic ability compared to the conventional size-based RECIST method.
Authors
- Xiaolei Xun
- Jie Zhou (ORCID: https://orcid.org/0000-0003-0755-4818)
- Ting Wei (ORCID: https://orcid.org/0000-0001-9008-0339)
- Geoffrey Kim
- Zhangsheng Yu (ORCID: https://orcid.org/0000-0002-8189-5330)
- Ruitian Gao
- Jin Zhang (ORCID: https://orcid.org/0009-0007-8921-7159)
- Yujia Xia (ORCID: https://orcid.org/0000-0003-1153-3833)
- Chao Liu (ORCID: https://orcid.org/0000-0002-4749-5499)
- Bobby Reddy
Institutions
- Shanghai Jiao Tong University (CN)
- BeiGene (China) (CN)
- Center for Life Sciences (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2024-08-26
- DOI
- https://doi.org/10.1186/s13244-024-01784-8
- Citations
- 19
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 7.68
Funders
- BeiGene
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality
- Shanghai Jiao Tong University