Machine learning-based integration of radiomics and dosiomics for early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma: A multicenter study.
- DOI
- 10.1002/acm2.70420
- Published
- 2026 Jan
- Container
- Journal of applied clinical medical physics
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1002/acm2.70420,
title = {Machine learning-based integration of radiomics and dosiomics for early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma: A multicenter study.},
author = {Sun X and Zhu H and Qiao J and Liang Y and Zhu Z and Dong S and Wang J},
year = {2026},
journal = {Journal of applied clinical medical physics},
doi = {10.1002/acm2.70420},
url = {https://doi.org/10.1002/acm2.70420}
}RIS
TY - JOUR TI - Machine learning-based integration of radiomics and dosiomics for early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma: A multicenter study. AU - Sun X AU - Zhu H AU - Qiao J AU - Liang Y AU - Zhu Z AU - Dong S AU - Wang J PY - 2026 JO - Journal of applied clinical medical physics DO - 10.1002/acm2.70420 UR - https://doi.org/10.1002/acm2.70420 ER -
APA
X, S., H, Z., J, Q., Y, L., Z, Z., S, D., & J, W. (2026). Machine learning-based integration of radiomics and dosiomics for early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma: A multicenter study.. Journal of applied clinical medical physics. https://doi.org/10.1002/acm2.70420
Source records
- pubmed · retrieved 2026-09-26T21:01:01.239Z