Deep Learning-based U-Mamba Model to Predict Differentiated Gastric Cancer using Radiomics Features from Spleen Segmentation
The radiomic features obtained through the automated segmentation of the spleen using deep learning demonstrate efficacy in predicting the degree of differentiation in GC. These features offer valuable guidance for clinical decision-making in the form of a nomogram.
Authors
- Zhendong Cao (ORCID: https://orcid.org/0009-0008-9844-9960)
- Shuangyan Xu (ORCID: https://orcid.org/0000-0001-9523-2266)
- Mingyu Li (ORCID: https://orcid.org/0000-0003-4812-8604)
- Lihang Xu
- Ying Tong (ORCID: https://orcid.org/0000-0001-6165-0291)
- Hui Shang (ORCID: https://orcid.org/0009-0002-5292-3969)
Institutions
- Affiliated Hospital of Chengde Medical College (CN)
Publication Details
- Journal
- Current Medical Imaging Formerly Current Medical Imaging Reviews
- Published
- 2024-12-20
- DOI
- https://doi.org/10.2174/0115734056349216241118115005
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00