{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T20:43:56Z","timestamp":1786653836659,"version":"build-2736575974"},"reference-count":34,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1]]},"abstract":"\n Purpose:<\/jats:title>\n \n This study aimed to develop a deep learning model for the prediction of V\n 20<\/jats:sub>\n (the volume of the lung parenchyma that received \u226520 Gy) during intensity-modulated radiation therapy using chest X-ray images.\n <\/jats:p>\n <\/jats:sec>\n \n Methods:<\/jats:title>\n \n The study utilized 91 chest X-ray images of patients with lung cancer acquired routinely during the admission workup. The prescription dose for the planning target volume was 60 Gy in 30 fractions. A convolutional neural network-based regression model was developed to predict V\n 20<\/jats:sub>\n . To evaluate model performance, the coefficient of determination\n (R<\/jats:italic>\n 2<\/jats:sup>\n ), root mean square error (RMSE), and mean absolute error (MAE) were calculated with conducting a four-fold cross-validation method. The patient characteristics of the eligible data were treatment period (2018\u20132022) and V\n 20<\/jats:sub>\n (19.3%; 4.9%\u201330.7%).\n <\/jats:p>\n <\/jats:sec>\n \n Results:<\/jats:title>\n \n The predictive results of the developed model for V\n 20<\/jats:sub>\n were 0.16, 5.4%, and 4.5% for the\n R<\/jats:italic>\n 2<\/jats:sup>\n , RMSE, and MAE, respectively. The median error was \u22121.8% (range, \u221213.0% to 9.2%). The Pearson correlation coefficient between the calculated and predicted V\n 20<\/jats:sub>\n values was 0.40. As a binary classifier with V\n 20<\/jats:sub>\n <20%, the model showed a sensitivity of 75.0%, specificity of 82.6%, diagnostic accuracy of 80.6%, and area under the receiver operator characteristic curve of 0.79.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions:<\/jats:title>\n \n The proposed deep learning chest X-ray model can predict V\n 20<\/jats:sub>\n and play an important role in the early determination of patient treatment strategies.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.4103\/jmp.jmp_122_23","type":"journal-article","created":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T01:00:25Z","timestamp":1711760425000},"page":"33-40","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-based Lung dose Prediction Using Chest X-ray Images in Non-small Cell Lung Cancer 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