Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.
- DOI
- 10.3389/frai.2023.1222612
- Published
- 2023
- Container
- Frontiers in artificial intelligence
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3389/frai.2023.1222612,
title = {Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.},
author = {Chaudhuri A and Pash G and Hormuth DA 2nd and Lorenzo G and Kapteyn M and Wu C and Lima EABF and Yankeelov TE and Willcox K},
year = {2023},
journal = {Frontiers in artificial intelligence},
doi = {10.3389/frai.2023.1222612},
url = {https://doi.org/10.3389/frai.2023.1222612}
}RIS
TY - JOUR TI - Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas. AU - Chaudhuri A AU - Pash G AU - Hormuth DA 2nd AU - Lorenzo G AU - Kapteyn M AU - Wu C AU - Lima EABF AU - Yankeelov TE AU - Willcox K PY - 2023 JO - Frontiers in artificial intelligence DO - 10.3389/frai.2023.1222612 UR - https://doi.org/10.3389/frai.2023.1222612 ER -
APA
A, C., G, P., 2nd, H. D., G, L., M, K., C, W., EABF, L., TE, Y., & K, W. (2023). Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.. Frontiers in artificial intelligence. https://doi.org/10.3389/frai.2023.1222612
Source records
- pubmed · retrieved 2026-09-26T02:41:28.199Z