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Dataset 1 (n\u2009=\u2009300) trained and tested the model. Dataset 2 (n\u2009=\u200910) evaluated contouring time and dosimetric parameters. Datasets 3 (n\u2009=\u200920) and 4 (n\u2009=\u200910) were for clinical evaluation. Dataset 5 (n\u2009=\u2009517) identified clinical factors influencing auto-contouring accuracy. Model performance was assessed using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The median DSC and HD95 for left- and right-sided models in Dataset 1 were 0.941, 1.75\u2009mm and 0.937, 2.47\u2009mm, respectively. In Dataset 2, both auto-contouring and auto-contouring with manual corrections were significantly faster than manual contouring (P\u2009=\u2009.005 for both), while still achieving clinically acceptable dosimetric results. In Dataset 3, two physicians rated automatic and manual contours as equivalent (P\u2009=\u2009.214, P\u2009=\u2009.075), while the other rated auto-contouring higher (P\u2009&amp;lt;\u2009.001). In Dataset 4, the auto-contouring model outperformed 1\/5 physicians by DSC (P\u2009=\u2009.009) and 3\/5 by HD95 (P\u2009=\u2009.015, P\u2009=\u2009.007, P\u2009=\u2009.017). In Dataset 5, peripheral tumor-bed and low-density breast tissue were associated with lower DSC (P\u2009&amp;lt;\u2009.001 for both) and higher HD95 (P\u2009&amp;lt;\u2009.001 for both). Cases without unfavorable factors performed better than those with (P\u2009&amp;lt;\u2009.001 for both).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed model demonstrated acceptable accuracy, consistency, and efficiency in breast CTV contouring. Peripheral tumor-bed and low-density breast tissue reduced auto-contouring performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>The characteristics of challenging cases in whole breast CTV auto-contouring should be identified.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjr\/tqag036","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:12:34Z","timestamp":1773213154000},"page":"942-950","source":"Crossref","is-referenced-by-count":0,"title":["Automatic segmentation of clinical target volume for radiation therapy in breast-conserving patients and exploration of clinical factors influential to its performance"],"prefix":"10.1093","volume":"99","author":[{"given":"Maochen","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiaotong University 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