{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T13:45:29Z","timestamp":1792071929049,"version":"4.4.0"},"reference-count":39,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA262017"],"award-info":[{"award-number":["R01CA262017"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA202761"],"award-info":[{"award-number":["R01CA202761"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Previous studies have shown that protecting airways connected to high\u2010functioning lung parenchyma reduces the risk of post\u2010treatment radiation\u2010induced lung injury. To account for airway sparing in radiotherapy planning, a critical requirement is to characterize the geometry and radiation response of individual airway segments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To report on automated individual airway labeling framework with branch dimensions extracted from CT\u2010derived virtual bronchoscopy images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed an automated graph\u2010based topological labeling algorithm with bifurcation detections and context\u2010based denoising. In\u2010house modules for estimating airway diameters and tube length were also developed. Both labeling and meta\u2010information estimation were validated against a commercial virtual bronchoscopy software in data from 20 lung cancer patients. Labeling metrics were segmentation surface scores: i.e., average symmetric surface distance (ASSD), normalized surface distance (NSD), and 95th percentile surface Hausdorff (HD95surf). Meta\u2010information metrics were absolute errors of branch major diameter, minor diameter, and tube length. Mean absolute error (MAE) and root mean square error (RMSE) were calculated to assess meta\u2010information deviations in both validations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Mean and standard deviations were 0.56 \u00b1 0.21\u00a0mm for ASSD, 87.66 \u00b1 3.20% for NSD, and 2.51 \u00b1 0.68\u00a0mm for HD95surf. Deviations of meta\u2010information metrics in labeling validation were MAE\/RMSE: 0.33 \u00b1 0.09\/0.67 \u00b1 0.16\u00a0mm for major diameter, 0.24 \u00b1 0.07\/0.53 \u00b1 0.15\u00a0mm for minor diameter, and 3.27 \u00b1 0.82\/5.53 \u00b1 1.53\u00a0mm for tube length. Deviations of meta\u2010information metrics in meta\u2010information validation were MAE\/RMSE: 1.81 \u00b1 0.30\/2.31 \u00b1 0.41\u00a0mm for major diameter, 1.25 \u00b1 0.27\/1.14 \u00b1 0.26\u00a0mm for minor diameter, and 2.20 \u00b1 0.32\/3.24 \u00b1 0.49\u00a0mm for tube length.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed open\u2010source framework provides stand\u2010alone, fully\u2010automated airway labeling with very good agreement with a commercial virtual bronchoscopy system. Our method shows strong potential for use in functionally\u2010guided lung sparing in radiation treatment plan optimization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70634","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T13:47:35Z","timestamp":1787838455000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Stand\u2010alone graph\u2010based lung airway labeling with quantitative validations"],"prefix":"10.1002","volume":"53","author":[{"given":"Hairong","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of Maryland School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arezoo","family":"Modiri","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Maryland School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit","family":"Sawant","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Maryland School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2023.01.005"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1200\/OP.2023.19.11_SUPPL.118"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.15326\/JCOPDF.2024.0489"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PRRO.2024.04.014"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2024.03.046"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2022.04.047"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2010.08.056"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2017.06.1815"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1259\/BJR.20190174"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.07.1233"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2022.11.026"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2024.02.019"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2022.01.046"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1136\/thorax\u20102023\u2010219767"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1097\/JTO.0b013e3181f1eaf3"},{"key":"e_1_2_8_17_1","unstructured":"CT Respiratory Analysis. 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