{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T03:02:25Z","timestamp":1789700545013,"version":"4.0.1"},"reference-count":35,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"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":["12405406"],"award-info":[{"award-number":["12405406"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate delineation of target volumes and organs\u2010at\u2010risk (OARs) is a critical yet labor\u2010intensive component of rectal cancer radiotherapy. While deep learning (DL)\u2010based automatic contouring systems are increasingly used to address inter\u2010observer variability and improve efficiency, artificial intelligence models require rigorous quality assurance and updates to reflect current technology. However, high\u2010level evidence regarding the longitudinal real\u2010world impact of implementing and iteratively updating these systems in clinical workflows is currently lacking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to evaluate the real\u2010world clinical impact of implementing and updating a DL\u2010based automatic contouring system in rectal cancer radiotherapy to generate high\u2010quality evidence of iterative updates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This longitudinal retrospective analysis included 150 patients divided into three cohorts: pre\u2010implementation (n1\u00a0=\u00a050), post\u2010implementation (n2\u00a0=\u00a050), and post\u2010update (n3\u00a0=\u00a050). Geometric similarities between unedited\u2010automatic and final treatment contours were compared across cohorts. Failure rates were systematically analyzed. Six oncologists contoured 21 additional cases through manual, first\u2010generation (Auto1), and second\u2010generation (Auto2) system\u2010assisted methods to evaluate contouring time, inter\u2010observer consistency, and accuracy. Additionally, a 5\u2010point Likert scale was used by two blinded senior oncologists to assess the clinical acceptability of the generated contours.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The mean Dice similarity coefficient (DSC) values of clinical target volume (CTV) before and after implementing the automatic contouring system were 0.87\u00a0\u00b1\u00a00.04 and 0.88\u00a0\u00b1\u00a00.04 (\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0=\u00a00.067), while those of OARs were 0.80\u00a0\u00b1\u00a00.06 and 0.88\u00a0\u00b1\u00a00.05 (\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&lt;\u00a00.001), respectively. Following the system update, they improved from 0.88\u00a0\u00b1\u00a00.04 to 0.93\u00a0\u00b1\u00a00.04 for CTV (\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&lt;\u00a00.001) and from 0.88\u00a0\u00b1\u00a00.05 to 0.95\u00a0\u00b1\u00a00.02 for OARs (\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&lt;\u00a00.001). The system update achieved an approximately 80.6% reduction in the mean failure rate. Auto2\u2010assisted method decreased the total time by approximately 58.8% compared with the manual method, and 21.9% compared with the Auto1\u2010assisted method. This method also demonstrated optimal inter\u2010observer consistency (0.95\u00a0\u00b1\u00a00.03) and accuracy (0.94\u00a0\u00b1\u00a00.03) for CTV. In the blinded clinical evaluation, 99.2% (125\/126) of the oncologist\u2010revised final contours received a Likert score of \u2265 4, and Auto2\u2010generated unedited contours showed significantly higher clinical acceptability than Auto1 (4.02\u00a0\u00b1\u00a00.25\u00a0vs. 3.26\u00a0\u00b1\u00a00.49,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&lt;\u00a00.001)\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Implementing an automatic contouring system provided crucial guidance for clinical practice. Its iterative update significantly reduced workload and inter\u2010observer variation while enhancing contouring efficiency and quality.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70768","type":"journal-article","created":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T04:31:51Z","timestamp":1787977911000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real\u2010world clinical impact of implementing and updating a deep learning\u2010based automatic contouring system in rectal cancer radiotherapy"],"prefix":"10.1002","volume":"27","author":[{"given":"Ningyu","family":"Wang","sequence":"first","affiliation":[{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College  Beijing 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