{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T06:45:36Z","timestamp":1782629136835,"version":"3.54.5"},"reference-count":22,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002642","name":"Korea University","doi-asserted-by":"publisher","award":["K2610281"],"award-info":[{"award-number":["K2610281"]}],"id":[{"id":"10.13039\/501100002642","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":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Auto\u2010segmentation tools are essential in adaptive radiation therapy (ART). While evaluation typically relies on geometric metrics like Dice similarity coefficient (DSC), high scores do not always translate to clinical acceptability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study investigated the \u201cillusory correlation\u201d between geometric indices and physician satisfaction and aimed to identify critical anatomical substructures that dictate clinical judgment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Heart auto\u2010segmentation was performed for 30 left\u2010sided breast cancer patients using pre\u2010built and on\u2010site trained fully convolutional dense network (FCDN) models. Geometric similarity was assessed via DSC, mean surface distance (MSD), and 95th percentile Hausdorff Distance (HD95). Clinical satisfaction was quantified through a physician\u2010blind test involving 17 anatomical items. Pearson correlation coefficients (PCC) and conditional probability (\n                      <jats:italic>P(B|A)<\/jats:italic>\n                      ) were used to analyze the relationship between substructure accuracy and overall clinical acceptance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Both models showed high geometric similarity (mean DSC \u223c 0.95), but clinical satisfaction differed drastically: the pre\u2010built model had a 3.3% acceptance rate, while the on\u2010site model achieved 93.3%. PCC analysis failed to show significant correlations between geometric metrics and satisfaction after multiple testing corrections. However, conditional probability analysis revealed that the cranial\/caudal borders and the superior vena cava (SVC) were the primary determinants of satisfaction, with\n                      <jats:italic>P(B|A)<\/jats:italic>\n                      values up to 0.95. Conversely, while coronary arteries showed the greatest geometric improvement in the on\u2010site model, their individual success did not guarantee overall clinical acceptance (\n                      <jats:italic>P(B|A)<\/jats:italic>\n                      \u223c 0.42\u20130.52), explaining the disconnect between standard metrics and clinical judgment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Geometric metrics alone are insufficient to validate auto\u2010segmentation for clinical use. Clinical acceptance is driven by specific critical substructures, particularly boundary regions, rather than global volumetric similarity. We propose a \u201ctwo\u2010guideline strategy\u201d that prioritizes these high\u2010impact regions and establishes quantitative gateways for model commissioning, providing a more robust framework for quality assurance (QA) in ART.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70642","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:19:11Z","timestamp":1781587151000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The illusory correlation between geometrical similarity and physician satisfaction in heart auto\u2010segmentation revealed by a physician\u2010blind test"],"prefix":"10.1002","volume":"27","author":[{"given":"Jae Choon","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, College of Medicine Korea University  Seoul Republic of Korea"},{"name":"Department of Medical Physics Kyonggi University  Gyeonggi Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun Jeong","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, College of Medicine Korea University  Seoul Republic of Korea"},{"name":"Department of Medical Physics Graduate School of Korea University  Sejong\u2010si Chungcheongnam\u2010do Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song Heui","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, College of Medicine Korea University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong Yun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Sales and CS OncoSoft  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyung Hwan","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Radiologic Science Far East University  Eumseong\u2010gun Chungcheongbuk\u2010do Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jang Bo","family":"Shim","sequence":"additional","affiliation":[{"name":"Department of Medical Physics Kyonggi University  Gyeonggi Republic of Korea"},{"name":"Department of Radiation Oncology Guro Hospital Korea University Medical Center  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nam Kwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, College of Medicine Korea University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3720-3448","authenticated-orcid":false,"given":"Suk","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, College of Medicine Korea University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2019.03.004"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.861857"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3109\/0284186X.2016.1173723"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16213670"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13337"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110609"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13040667"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1460396922000437"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.580919"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12094\u2010020\u201002394\u20108"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.2307\/1932409"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.640755"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.232073"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010021\u201001771\u2010z"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2021.12.017"},{"key":"e_1_2_9_17_1","doi-asserted-by":"crossref","unstructured":"J\u00e9gouS DrozdzalM VazquezD RomeroA BengioY.The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2017. pp.11\u201019.","DOI":"10.1109\/CVPRW.2017.156"},{"key":"e_1_2_9_18_1","unstructured":"KongFM QuintL MachtayM et\u00a0al.Atlases for organs at risk (OARs) in thoracic radiation therapy. 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