A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer
The superior performance demonstrates the potential of our work for predicting RCLM, suggesting its potential assistance in personalized treatment and follow-up plans.
Authors
- Ruhui Xiao
- Liangyou Zhang
- Lin Yao (ORCID: https://orcid.org/0000-0002-8256-6862)
- You Zhou (ORCID: https://orcid.org/0000-0003-0013-1281)
- Calvin R. Wei (ORCID: https://orcid.org/0000-0003-1036-3342)
- Liming Zhong (ORCID: https://orcid.org/0000-0002-0048-7838)
- Yun Wan
- Shuang Li (ORCID: https://orcid.org/0000-0002-6828-5925)
- Li Yin (ORCID: https://orcid.org/0009-0006-5272-7078)
- Wei Yang (ORCID: https://orcid.org/0000-0003-2945-2898)
- Xi Li (ORCID: https://orcid.org/0000-0003-3639-1907)
- Yongju Yi
Institutions
- Sun Yat-sen University (CN)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- Key Laboratory of Guangdong Province (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-02-06
- DOI
- https://doi.org/10.3389/fonc.2025.1496820
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Basic and Applied Basic Research Foundation of Guangdong Province