{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T07:32:20Z","timestamp":1753860740612,"version":"3.41.2"},"reference-count":28,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB022911"],"award-info":[{"award-number":["R01EB022911"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007210","name":"Varian Medical Systems","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007210","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":[[2023,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Variability in contouring contributes to large variations in radiation therapy planning and treatment outcomes. The development and testing of tools to automatically detect contouring errors require a source of contours that includes well\u2010understood and realistic errors. The purpose of this work was to develop a simulation algorithm that intentionally injects errors of varying magnitudes into clinically accepted contours and produces realistic contours with different levels of variability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used a dataset of CT scans from 14 prostate cancer patients with clinician\u2010drawn contours of the regions of interest (ROI) of the prostate, bladder, and rectum. Using our newly developed Parametric Delineation Uncertainties Contouring (PDUC) model, we automatically generated alternative, realistic contours. The PDUC model consists of the contrast\u2010based DU generator and a 3D smoothing layer. The DU generator transforms contours (deformation, contraction, and\/or expansion) as a function of image contrast. The generated contours undergo 3D smoothing to obtain a realistic look. After model building, the first batch of auto\u2010generated contours was reviewed. Editing feedback from the reviews was then used in a filtering model for the auto\u2010selection of clinically acceptable (minor\u2010editing) DU contours.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Overall, C values of 5 and 50 consistently produced high proportions of minor\u2010editing contours across all ROI compared to the other C values (0.936 0.111 and 0.552 0.228, respectively). The model performed best on the bladder, which had the highest proportion of minor\u2010editing contours (0.606) of the three ROI. In addition, the classification AUC for the filtering model across all three ROI is 0.724 0.109.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The proposed methodology and subsequent results are promising and could have a great impact on treatment planning by generating mathematically simulated alternative structures that are clinically relevant and realistic enough (i.e., similar to clinician\u2010drawn contours) to be used in quality control of radiation therapy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.13970","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T09:41:38Z","timestamp":1681983698000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parametric delineation uncertainties contouring (PDUC) modeling on CT scans of prostate cancer patients"],"prefix":"10.1002","volume":"24","author":[{"given":"Vi","family":"Ly","sequence":"first","affiliation":[{"name":"Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"},{"name":"Department of Biostatistics and Data Science University of Texas Health Science Center School of Public Health  Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Data Science University of Texas Health Science Center School of Public Health  Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos","family":"Cardenas","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sean","family":"Maroongroge","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brian","family":"De","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel El","family":"Basha","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurence","family":"Court","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas 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