Predicting tumor mutation burden and <scp>VHL</scp> mutation from renal cancer pathology slides with self‐supervised deep learning
Our results indicated that SSL-ABMIL can effectively extract histological features for predicting TMB and VHL mutation, demonstrating promising results in linking tumor morphology and molecular biology.
Authors
- Qingyuan Zheng (ORCID: https://orcid.org/0000-0003-4682-3857)
- Zhiyuan Chen (ORCID: https://orcid.org/0000-0002-6820-3678)
- Zhuoni Xiao (ORCID: https://orcid.org/0000-0002-2030-6045)
- Xiuheng Liu (ORCID: https://orcid.org/0000-0003-3882-2715)
- Jingping Yuan (ORCID: https://orcid.org/0000-0001-7470-6899)
- Rui Yang (ORCID: https://orcid.org/0000-0001-6134-3400)
- Junjie Fan
- Lei Wang (ORCID: https://orcid.org/0009-0001-7791-0809)
- Xinyu Wang (ORCID: https://orcid.org/0009-0004-9019-4810)
Institutions
- Wuhan University (CN)
- Institute of Software (CN)
- Renmin Hospital of Wuhan University (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2024-08-01
- DOI
- https://doi.org/10.1002/cam4.70112
- Citations
- 7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.83