{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T11:59:48Z","timestamp":1790251188430,"version":"4.1.0"},"reference-count":28,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011699","name":"Siemens Healthineers","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011699","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["5P30CA056036\u201017"],"award-info":[{"award-number":["5P30CA056036\u201017"]}],"id":[{"id":"10.13039\/100000054","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":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To evaluate the clinical feasibility of the Siemens Healthineers AI\u2010Rad Companion Organs RT VA30A (Organs\u2010RT) auto\u2010contouring algorithm for organs at risk (OARs) of the pelvis, thorax, and head and neck (H&amp;N).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Computed tomography (CT) datasets from 30 patients (10 pelvis, 10 thorax, and 10 H&amp;N) were collected. Four sets of OARs were generated on each scan, one set by Organs\u2010RT and the others by three experienced users independently. A physician (expert) then evaluated each contour by assigning a score from the following scale: 1\u2010Must Redo, 2\u2010Major Edits, 3\u2010Minor Edits, 4\u2010Clinically usable. Using the highest\u2010scored OAR from the human users as a reference, the contours generated by Organs\u2010RT were evaluated via Dice Similarity Coefficient (DSC), Hausdorff Distance (HDD), Mean Distance to Agreement (mDTA), Volume comparison, and visual inspection. Additionally, each human user recorded the time to delineate each structure set and time\u2010saving efficiency was measured.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The average DSC obtained for the pelvic OARs ranged between (0.81 \u00b1 0.06)<jats:sub>Rectum<\/jats:sub> and (0.94 \u00b1 0.03)<jats:sub>Bladder<\/jats:sub>. (0.75 \u00b1 0.09)<jats:sub>Esophagus<\/jats:sub> to  for the thoracic OARs and (0.66 \u00b1 0.07)<jats:sub>Lips<\/jats:sub> to (0.83 \u00b1 0.04)<jats:sub>Brainstem<\/jats:sub> for the H&amp;N. The average HDD in cm for the pelvis cohort ranged between (0.95 \u00b1 0.35)<jats:sub>Bladder<\/jats:sub> to (3.62 \u00b1 2.50)<jats:sub>Rectum<\/jats:sub>, (0.42 \u00b1 0.06)<jats:sub>SpinalCord<\/jats:sub> to (2.09 \u00b1 2.00)<jats:sub>Esophagus<\/jats:sub> for the thoracic set and  to (1.50 \u00b1 0.50)<jats:sub>Mandible<\/jats:sub> for the H&amp;N region. The time\u2010saving efficiency was 67% for H&amp;N, 83% for pelvis, and 84% for thorax. 72.5%, 82%, and 50% of the pelvis, thorax, and H&amp;N OARs were scored as clinically usable by the expert, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The highest agreement registered between OARs generated by Organs\u2010RT and their respective references was for the bladder, heart, lungs, and femoral heads, with an overall DSC\u22650.92. The poorest agreement was for the rectum, esophagus, and lips, with an overall DSC\u2a7d0.81. Nonetheless, Organs\u2010RT serves as a reliable auto\u2010contouring tool by minimizing overall contouring time and increasing time\u2010saving efficiency in radiotherapy treatment planning.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14090","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T04:59:52Z","timestamp":1689742792000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Feasibility evaluation of novel AI\u2010based deep\u2010learning contouring algorithm for radiotherapy"],"prefix":"10.1002","volume":"24","author":[{"given":"Luis A.","family":"Maduro Bustos","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Christiana Care Helen F. 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