Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer.
- DOI
- 10.1093/bjr/tqag129
- Published
- 2026-09-01
- Container
- Br J Radiol
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1093/bjr/tqag129,
title = {Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer.},
author = {Nakajo M and Hirahara D and Baba K and Hirahara M and Eizuru Y and Tani A and Takumi K and Kamimura K and Kanzaki F and Ohtsuka T and Yoshiura T.},
year = {2026},
journal = {Br J Radiol},
doi = {10.1093/bjr/tqag129},
url = {https://doi.org/10.1093/bjr/tqag129}
}RIS
TY - JOUR TI - Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer. AU - Nakajo M AU - Hirahara D AU - Baba K AU - Hirahara M AU - Eizuru Y AU - Tani A AU - Takumi K AU - Kamimura K AU - Kanzaki F AU - Ohtsuka T AU - Yoshiura T. PY - 2026 JO - Br J Radiol DO - 10.1093/bjr/tqag129 UR - https://doi.org/10.1093/bjr/tqag129 ER -
APA
M, N., D, H., K, B., M, H., Y, E., A, T., K, T., K, K., F, K., T, O., & T., Y. (2026). Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer.. Br J Radiol. https://doi.org/10.1093/bjr/tqag129
Source records
- europe-pmc · retrieved 2026-09-26T22:05:40.177Z