A multidimensional data-driven approach to surgical plan optimization and postoperative residual tumor prediction in ovarian cancer
This study developed a novel deep learning-based model to predict postoperative residual tumor probability, integrating clinical and immune microenvironment data. While the model excelled in identifying high-risk cases (e.g., R2), further optimization is needed to improve R0 and R1 differentiation. Future research should expand datasets and integrate multi-omics data to enhance predictive accuracy and clinical applicability.
Authors
- Tianhui He (ORCID: https://orcid.org/0000-0002-2835-0702)
- Hongyan Guo (ORCID: https://orcid.org/0000-0001-6917-5341)
- Qinkun Sun
- Chunliang Shang (ORCID: https://orcid.org/0000-0003-0275-7894)
- Yi Xie (ORCID: https://orcid.org/0009-0003-3485-6843)
- Jianling Yang (ORCID: https://orcid.org/0000-0003-3260-4346)
- Ping Yu
- Lin Yang (ORCID: https://orcid.org/0000-0001-6185-5713)
- Jing Wang (ORCID: https://orcid.org/0000-0002-3793-3264)
Institutions
- Peking University (CN)
- Massachusetts General Hospital (US)
- Peking University Third Hospital (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-12-17
- DOI
- https://doi.org/10.3389/fimmu.2025.1705428
- Primary Topic
- Ferroptosis and cancer prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Peking University
- Peking University Third Hospital