{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:16:59Z","timestamp":1791307019569,"version":"4.3.1"},"reference-count":33,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076256"],"award-info":[{"award-number":["62076256"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272161"],"award-info":[{"award-number":["62272161"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12005306"],"award-info":[{"award-number":["12005306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The use of convolution neural networks (CNN) to accurately predict dose distributions can accelerate intensity\u2010modulated radiation therapy (IMRT) planning. The purpose of our study is to develop a novel deep learning architecture for precise voxel\u2010level dose prediction on brain tumors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A dataset of 120 patients with brain tumors is built for the retrospective study. The dose distributions are predicted by a designed end\u2010to\u2010end model called TS\u2010Net, in which the transformer encoder module is utilized to obtain abundant global features by learning long\u2010range correlations of the input sequence. In addition, semantic field alignment (SFA) block is proposed in decoding path to ensure effective propagation of strong semantic information from deep to shallow. Five images from different channels are fed into the architecture, including a computed tomography (CT) image, a planning target volumes (PTV) image, an organs\u2010at\u2010risk (OARs) image, a beam configuration image, and a distance image, and the predicted dose distributions are taken as outputs. We use different evaluation metrics to evaluate the performance of the model and discuss the role of the auxiliary beam configuration information provided by non\u2010modulated dose distributions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The TS\u2010Net prediction accuracies in terms of mean absolute error (MAE) are 2.98% for PTV, 7.19% for brainstem, 1.88% for left len, 2.48% for right len, 9.61% for left optic nerve, 9.10% for right optic nerve, 8.99% for optic chiasma, and 8.28% for pituitary. There is no statistically significant difference between the predicted results and clinical dose distributions for clinical indexes including homogeneity index (HI), D50, and D95 for PTV; V40, mean dose, and max dose for OARs; except for conformation index (CI) and D2 for PTV. The model has dice similarity coefficient (DSC) values of above 0.91 for most isodose volumes, clearly outperforming HD U\u2010Net, and being slightly better than U\u2010Net and DCNN.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed TS\u2010Net with beam configuration input can achieve accurate voxel\u2010level dose prediction for brain tumors, and is a usable tool for improving the efficiency and quality of radiotherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.16122","type":"journal-article","created":{"date-parts":[[2022,11,26]],"date-time":"2022-11-26T16:59:12Z","timestamp":1669481952000},"page":"1149-1161","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Deep learning architecture with transformer and semantic field alignment for voxel\u2010level dose prediction on 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