{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T15:41:56Z","timestamp":1778427716787,"version":"3.51.4"},"reference-count":35,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"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":["62371243"],"award-info":[{"award-number":["62371243"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20231190"],"award-info":[{"award-number":["BK20231190"]}],"id":[{"id":"10.13039\/501100004608","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":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The delineation of organs at risk (OARs) and clinical target volume (CTV) is an important step in adaptive radiotherapy (ART). Cone\u2010beam computed tomography (CBCT) images are easy to obtain in radiotherapy (RT).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aims to develop an effective CBCT\u2010guided delineation method for breast cancer ART.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 60 planning CT images and 330 CBCT images from 60 patients with breast cancer who underwent breast\u2010conserving surgery were used to develop uncertainty\u2010guided multitask semi\u2010supervised network (UGMNet), which is guided by model segmentation uncertainty and uses intra\u2010task consistency learning to effectively utilize unlabeled data. Branch networks were added to take advantage of the region\u2010level shape constraints and bounding\u2010level distance information of the data. UGMNet was trained with 20% labeled data and a large amount of unlabeled data to obtain the generalized segmentation model (Ours\u2010G). The planning CT image of each patient was then inputted to the generalized model for fine\u2010tuning to establish a personal segmentation model (Ours\u2010P). We compared Ours\u2010G and Ours\u2010P with three classical semi\u2010supervised methods, uncertainty aware mean teacher (UA\u2010MT), dual\u2010task consistency network (DTC), and mutual consistency network (MC\u2010Net+).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared with other semi\u2010supervised segmentation model, our proposed method achieved better or equivalent segmentation performance under the same backbone network (3D VNet) and task setting. For CTV delineation, the mean Dice similarity coefficient (DSC) of UA\u2010MT, DTC, MC\u2010Net+, Ours\u2010G, and Ours\u2010P were 0.84, 0.80, 0.60, 0.84, and 0.87, respectively. For the heart, the mean DSC values were 0.82, 0.85, 0.72, 0.86, and 0.89, respectively. For the left lung, the mean DSC values were 0.92, 0.93, 0.91, 0.94, and 0.92, respectively. For the right lung, the mean DSC values were 0.96, 0.94, 0.93, 0.97, and 0.96, respectively. For the spinal cord, the mean DSC values were 0.73, 0.72, 0.77, 0.80, and 0.80, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed method realizes effective delineation for CBCT\u2010guided ART using a small amount of labeled data and improves the segmentation accuracy of CTV and OARs on CBCT images using personalized modeling.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17728","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T04:53:51Z","timestamp":1741582431000},"page":"4147-4158","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Uncertainty\u2010guided multitask semi\u2010supervised network for the multi\u2010organ segmentation of breast cancer 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