Predicting bone metastasis risk of colorectal tumors using radiomics and deep learning ViT model
The integration of radiomics with ViT-based deep learning offers a robust and accurate method for predicting bone metastasis risk in colorectal cancer patients. The ViT model's ability to analyze dual-modality CT imaging data provides greater precision in risk assessment, which can improve clinical decision-making and personalized treatment strategies. These findings underscore the promise of advanced deep learning models in enhancing the accuracy of metastasis prediction. Further validation in larger, multicenter studies is recommended to confirm the generalizability of these results.
Authors
- Wenxi Liu
- Risheng Huang (ORCID: https://orcid.org/0000-0001-6661-023X)
- Guanfeng Chen
- D. Ye
- Jie Zhang (ORCID: https://orcid.org/0000-0002-2497-725X)
- Jieyun Chen (ORCID: https://orcid.org/0000-0002-4129-1348)
- Yung‐Hsiang Lin (ORCID: https://orcid.org/0009-0001-6771-5911)
- Jing Huang (ORCID: https://orcid.org/0009-0001-4037-5283)
Institutions
- Fujian Medical University (CN)
- Second Affiliated Hospital of Fujian Medical University (CN)
Publication Details
- Journal
- Journal of bone oncology
- Published
- 2024-12-31
- DOI
- https://doi.org/10.1016/j.jbo.2024.100659
- Citations
- 10
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.04
Funders
- Natural Science Foundation of Fujian Province
- Quanzhou City Science and Technology Program