{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T18:10:07Z","timestamp":1792001407316,"version":"4.4.0"},"reference-count":40,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["R37CA250921"],"award-info":[{"award-number":["R37CA250921"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["R01CA261964"],"award-info":[{"award-number":["R01CA261964"]}],"id":[{"id":"10.13039\/100000001","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":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Compared to photon therapy (XT), proton therapy (PT) can often reduce normal tissue toxicity for head and neck (HN) cancer patients, despite being a limited resource. On the other hand, clinical decision\u2010making process to select between PT and XT (e.g., treatment planning and then plan evaluation for comparing normal tissue complication probabilities (NTCP) between XT and PT) is time\u2010consuming and resource demanding.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to develop and validate the feasibility of an artificial intelligence (AI)\u2010based automated method for efficient patient selection between PT and XT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A heterogeneous cohort of 104 bilateral HN patients with auto\u2010planned PT and XT plans was analyzed, covering diverse tumor subsites and prescription dose levels. To ensure accurate dose and NTCP prediction, a joint\u2010modality prediction framework was developed, incorporating a 3D attention\u2010gated U\u2010net with a multi\u2010constrained loss function. A stratified 10\u2010fold cross\u2010validation strategy was employed to evaluate and compare model performance. The NTCP differences between XT and PT for grade II\/III xerostomia\/dysphagia exceeding certain thresholds are used to select patients for PT according to the Landelijk Indicatie Protocol Protonentherapie (versie 2.2) (LIPPv2.2).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI\u2010assisted patient selection process took about 10.1 s per patient. Our method achieved an accuracy of 85.58% and a weighted accuracy of 81.11% in patient selection. For dysphagia grades \u2265 2 and \u2265 3, the predicted results exhibited consistent selection with the ground truth in 86.54% and 89.42% of cases, respectively. Compared to previous models, the average \u0394NTCP prediction error (\u0394NTCP ground truth\u2014\u0394NTCP predicted, mean\u00a0\u00b1\u00a0SD) of the proposed method was 1.47\u00a0\u00b1\u00a01.80%, statistically lower than U\u2010net (1.67\u00a0\u00b1\u00a02.20%) and hierarchically densely connected U\u2010net (2.34\u00a0\u00b1\u00a03.25%). Moreover, the joint\u2010modality prediction of PT and XT dose distributions using Attention U\u2010net achieved comparable performance to separate single\u2010modality predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study highlights the potential of a novel AI\u2010assisted framework with joint\u2010modality prediction to enhance efficiency and precision for proton\u2013photon patient selection in the heterogeneous dataset, demonstrating the generalizability and robustness of the proposed approach.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70183","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T04:06:38Z","timestamp":1764907598000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient proton\u2013photon patient selection via 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