{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T13:20:25Z","timestamp":1784640025479,"version":"3.55.0"},"reference-count":34,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"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":["12205375"],"award-info":[{"award-number":["12205375"]}],"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":["11975313"],"award-info":[{"award-number":["11975313"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005090","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["Z201100006820058"],"award-info":[{"award-number":["Z201100006820058"]}],"id":[{"id":"10.13039\/501100005090","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":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>It is necessary to contour regions of interest (ROIs) for online magnetic resonance imaging (MRI)\u2010guided adaptive radiotherapy (MRIgART). These updated contours are used for online replanning to obtain maximum dosimetric benefits. Contouring can be accomplished using deformable image registration (DIR) and deep learning (DL)\u2010based autosegmentation methods. However, these methods may require considerable manual editing and thus prolong treatment time.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The present study aimed to improve autosegmentation performance by integrating patients\u2019 pretreatment information in a DL\u2010based segmentation algorithm. It is expected to improve the efficiency of current MRIgART process.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Forty patients with prostate cancer were enrolled retrospectively. The online adaptive MR images, patient\u2010specific planning computed tomography (CT), and contours in CT were used for segmentation. The deformable registration of planning CT and MR images was performed first to obtain a deformable CT and corresponding contours. A novel DL network, which can integrate such patient\u2010specific information (deformable CT and corresponding contours) into the segmentation task of MR images was designed. We performed a four\u2010fold cross\u2010validation for the DL models. The proposed method was compared with DIR and DL methods on segmentation of prostate cancer. The ROIs included the clinical target volume (CTV), bladder, rectum, left femur head, and right femur head. Dosimetric parameters of automatically generated ROIs were evaluated using a clinical treatment planning system.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed method enhanced the segmentation accuracy of conventional procedures. Its mean value of the dice similarity coefficient (93.5%) over the five ROIs was higher than both DIR (87.5%) and DL (87.2%). The number of patients (<jats:italic>n<\/jats:italic>\u00a0=\u00a040) that required major editing using DIR, DL, and our method were 12, 18, and 7 (CTV); 17, 4, and 1 (bladder); 8, 11, and 5 (rectum); 2, 4, and 1 (left femur head); and 3, 7, and 1 (right femur head), respectively. The Spearman rank correlation coefficient of dosimetry parameters between the proposed method and ground truth was 0.972\u00a0\u00b1\u00a00.040, higher than that of DIR (0.897\u00a0\u00b1\u00a00.098) and DL (0.871\u00a0\u00b1\u00a00.134).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study proposed a novel method that integrates patient\u2010specific pretreatment information into DL\u2010based segmentation algorithm. It outperformed baseline methods, thereby improving the efficiency and segmentation accuracy in adaptive radiotherapy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16608","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T10:29:45Z","timestamp":1689330585000},"page":"922-932","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Pretreatment information\u2013aided automatic segmentation for online magnetic resonance imaging\u2010guided prostate radiotherapy"],"prefix":"10.1002","volume":"51","author":[{"given":"Bining","family":"Yang","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":"Yuxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"National 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