Multiview deep-learning-enabled histopathology for prognostic and therapeutic stratification in stage II colorectal cancer: A retrospective multicenter study
Together, these results highlight the potential utility of deep learning-based histopathological analysis for automated risk stratification in stage II CRC. In particular, our findings support the relevance of TLSs as a histological biomarker with potential implications for personalizing ACT decisions.
Authors
- Xueyi Zheng (ORCID: https://orcid.org/0000-0003-1386-7052)
- Mengke Ma (ORCID: https://orcid.org/0009-0008-8115-0559)
- Ruixuan Wang (ORCID: https://orcid.org/0000-0002-8714-0369)
- XIAOBO WEN
- Donghui Huang
- P Hu
- Z. Zhao
- Muyan Cai
- Dexia Chen
- Yuqian Zhang
- Shasha Liu
- Xiaosheng He
- Hao Chen
- Xinke Zhang
- Yan Sun
- Chengyou Zheng
Institutions
- Johns Hopkins University (US)
- Sun Yat-sen University (CN)
- Johns Hopkins Medicine (US)
- Tianjin Medical University Cancer Institute and Hospital (CN)
- Fudan University Shanghai Cancer Center (CN)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- Sun Yat-sen University Cancer Center (CN)
Publication Details
- Journal
- PLoS Medicine
- Published
- 2026-01-13
- DOI
- https://doi.org/10.1371/journal.pmed.1004614
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00