{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T20:35:05Z","timestamp":1792096505189,"version":"4.4.0"},"reference-count":27,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12275357"],"award-info":[{"award-number":["12275357"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC2411600"],"award-info":[{"award-number":["2023YFC2411600"]}],"id":[{"id":"10.13039\/501100012166","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":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate dose prediction is challenged by the lack of available training samples and the rapid evolution of radiotherapy techniques.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>A cross\u2010technique transfer learning strategy was developed to predict the dose distribution for radiotherapy planning using limited training samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were collected from 154 patients with nasopharyngeal carcinoma: 60 treated with intensity\u2010modulated radiotherapy (IMRT) and 94 treated with volumetric modulated arc therapy (VMAT). The Res\u2010U Net was selected as the base deep learning network. Cross\u2010technique models were pretrained on the IMRT dataset and subsequently fine\u2010tuned on VMAT data using limited samples (five and seven cases). Independent models were trained from scratch using the same limited samples, while a standard model trained on the full VMAT training set served as the reference. Model performance was evaluated on a test set using metrics including the dose\u2013volume histogram (DVH), voxel\u2010based mean absolute error (MAE), and the Dice similarity coefficient (DSC) of the isodose volume.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The cross\u2010technique models exhibited clinically acceptable performance with only five training samples and were comparable to the standard model (MAE deviation: 0.15%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&gt;\u00a00.01 after Bonferroni correction; DSC deviation: 0.11%\u20130.72%). Performance improved further with seven training samples (MAE deviation: 0.05%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&gt;\u00a00.01; DSC deviation: 0.02%\u20130.40%). However, the independent models trained with five or seven samples showed significantly inferior performance (five samples: MAE deviation: 1.14%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01, DSC deviation: 0.98%\u20132.48%; seven samples: MAE deviation: 0.50%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01, DSC deviation: 0.48%\u20131.05%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The cross\u2010technique models accurately and reliably predicted the dose distribution for a new radiotherapy technique using a limited sample size.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70346","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T04:03:21Z","timestamp":1773201801000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Cross\u2010technique transfer learning to predict the dose distribution for radiotherapy planning based on a limited sample size"],"prefix":"10.1002","volume":"53","author":[{"given":"Xiaohong","family":"Wang","sequence":"first","affiliation":[{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Wang","sequence":"additional","affiliation":[{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College  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