Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers
Radiomics and deep learning features derived from automated spleen segmentation to construct a nomogram demonstrate efficacy in predicting serosal invasion in GC. Concurrently, fully automated segmentation provides a novel and reproducible approach for radiomics research.
Authors
- Zhendong Cao (ORCID: https://orcid.org/0009-0008-9844-9960)
- Dong Han (ORCID: https://orcid.org/0000-0003-4010-1220)
- Bin Zhao (ORCID: https://orcid.org/0000-0001-7919-6680)
- Lihang Xu
- Hui Shang (ORCID: https://orcid.org/0009-0002-5292-3969)
- Tao Feng (ORCID: https://orcid.org/0009-0006-9560-5441)
- Fengying Liang
Institutions
- Chengde Medical University (CN)
- Chengde Medical University (CN)
Publication Details
- Journal
- Journal of Cancer Research and Clinical Oncology
- Published
- 2025-02-03
- DOI
- https://doi.org/10.1007/s00432-025-06117-w
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80