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Reducing the dose delivered to high functioning regions may reduce the occurrence radiation\u2010induced lung injuries (RILIs) and toxicities. However, in order to develop effective lung function\u2010sparing plans, accurate predictions of post\u2010RT ventilation change are needed to determine which regions of the lung should be spared.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To predict pulmonary ventilation change following RT for nonsmall cell lung cancer using machine learning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A conditional generative adversarial network (cGAN) was developed with data from 82 human subjects enrolled in a randomized clinical trial approved by the institution's IRB to predict post\u2010RT pulmonary ventilation change. The inputs to the network were the pre\u2010RT pulmonary ventilation map and radiation dose distribution. The loss function was a combination of the binary cross\u2010entropy loss and an asymmetrical structural similarity index measure (aSSIM) function designed to increase penalization of under\u2010prediction of ventilation damage. Network performance was evaluated against a previously developed polynomial regression model using a paired sample <jats:italic>t<\/jats:italic>\u2010test for comparison. Evaluation was performed using eight\u2010fold cross\u2010validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>From the eight\u2010fold cross\u2010validation, we found that relative to the polynomial model, the cGAN model significantly improved predicting regions of ventilation damage following radiotherapy based on true positive rate (TPR), 0.14\u00b10.15 to 0.72\u00b10.21, and Dice similarity coefficient (DSC), 0.19\u00b10.16 to 0.46\u00b10.14, but significantly declined in true negative rate, 0.97\u00b10.05 to 0.62\u00b10.21, and accuracy, 0.79\u00b10.08 to 0.65\u00b10.14. Additionally, the average true positive volume increased from 104\u00b1119 cc in the POLY model to 565\u00b1332 cc in the cGAN model, and the average false negative volume decreased from 654\u00b1361 cc in the POLY model to 193\u00b1163 cc in the cGAN model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed cGAN model demonstrated significant improvement in TPR and DSC. The higher sensitivity of the cGAN model can improve the clinical utility of functional lung avoidance RT by identifying larger volumes of functional lung that can be spared and thus decrease the probability of the patient developing\u00a0RILIs.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16311","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T13:39:00Z","timestamp":1676295540000},"page":"3199-3209","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Predicting pulmonary ventilation damage after radiation therapy for nonsmall cell lung cancer using a ResNet generative adversarial network"],"prefix":"10.1002","volume":"50","author":[{"given":"Eric M.","family":"Wallat","sequence":"first","affiliation":[{"name":"Department of Medical Physics University of Wisconsin\u2014Madison  Madison Wisconsin USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonia E.","family":"Wuschner","sequence":"additional","affiliation":[{"name":"Department of Medical Physics University of Wisconsin\u2014Madison  Madison Wisconsin USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mattison J.","family":"Flakus","sequence":"additional","affiliation":[{"name":"Department of Medical Physics University of Wisconsin\u2014Madison  Madison Wisconsin USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah E.","family":"Gerard","sequence":"additional","affiliation":[{"name":"Roy J. 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