{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T03:02:01Z","timestamp":1789700521290,"version":"4.0.1"},"reference-count":33,"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\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["24K18818"],"award-info":[{"award-number":["24K18818"]}],"id":[{"id":"10.13039\/501100001691","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>Pulmonary ventilation imaging has become an increasingly important component in thoracic radiotherapy, as it is applied to functional avoidance radiotherapy, where radiation doses are strategically minimized to preserve functional lung regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We developed a framework that generates computed tomography ventilation images (CTVI) from planning CT (PCT) images and demonstrated its estimation performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The subjects were a patient cohort consisting of 102 locally\u2010advanced non\u2010small\u2010cell lung cancer (NSCLC) patients who received radiotherapy from 2014 to 2023. The PCT images were acquired while ensuring the absence of baseline drift, frequency variation, amplitude changes, and additive random observation noise using a real\u2010time position management (RPM) system to minimize the introduction of inaccuracies into PCT images. On the day of PCT scan, CTVI based on four\u2010dimensional computed tomography images () images was also generated using both deformable image registration and the computing methods employed in the VAMPIRE study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The CTVI based on PCT () estimated by a model trained via hyperparameter optimization of a U\u2010net deep neural network with 5\u2010fold cross\u2010validation was compared with . In 5\u2010fold cross\u2010validation, the average voxel\u2010wise Spearman's correlation coefficient (\n                      <jats:italic>r<\/jats:italic>\n                      <jats:sub>s<\/jats:sub>\n                      )\u00a0\u00b1\u00a0one standard deviation between  and  was 0.77\u00a0\u00b1\u00a00.08. The Dice similarity coefficient (DSC) was computed for three functional regions (high, moderate, and low), each delineated by approximately equal volumes, obtaining DSC\n                      <jats:sub>high<\/jats:sub>\n                      , DSC\n                      <jats:sub>moderate<\/jats:sub>\n                      , and DSC\n                      <jats:sub>low<\/jats:sub>\n                      values of 0.68\u00a0\u00b1\u00a00.06, 0.51\u00a0\u00b1\u00a00.08, and 0.75\u00a0\u00b1\u00a00.04, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We successfully developed a framework that estimates , and demonstrated its estimation performance in terms of Spearman's correlation coefficient and DSC on locally\u2010advanced NSCLC patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70777","type":"journal-article","created":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T04:43:46Z","timestamp":1787978626000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pulmonary ventilation images from planning CT images using deep learning on locally\u2010advanced NSCLC patients"],"prefix":"10.1002","volume":"27","author":[{"given":"Yoshiyuki","family":"Katsuta","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Tohoku University Graduate School of Medicine  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taichi","family":"Hoshino","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Tohoku University Graduate School of Medicine  Sendai 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