Pulmonary ventilation images from planning CT images using deep learning on locally‐advanced NSCLC patients

Abstract

Abstract Background 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. Purpose We developed a framework that generates computed tomography ventilation images (CTVI) from planning CT (PCT) images and demonstrated its estimation performance. Methods The subjects were a patient cohort consisting of 102 locally‐advanced non‐small‐cell 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‐time position management (RPM) system to minimize the introduction of inaccuracies into PCT images. On the day of PCT scan, CTVI based on four‐dimensional computed tomography images (CTVI4DCTDIR) images was also generated using both deformable image registration and the computing methods employed in the VAMPIRE study. Results The CTVI based on PCT (CTVIPCTDNN) estimated by a model trained via hyperparameter optimization of a U‐net deep neural network with 5‐fold cross‐validation was compared with CTVI4DCTDIR. In 5‐fold cross‐validation, the average voxel‐wise Spearman's correlation coefficient (r s) ± one standard deviation between CTVIPCTDNN and CTVI4DCTDIR was 0.77 ± 0.08. The Dice similarity coefficient (DSC) was computed for three functional regions (high, moderate, and low), each delineated by approximately equal volumes, obtaining DSChigh, DSCmoderate, and DSClow values of 0.68 ± 0.06, 0.51 ± 0.08, and 0.75 ± 0.04, respectively. Conclusions We successfully developed a framework that estimates CTVIPCTDNN, and demonstrated its estimation performance in terms of Spearman's correlation coefficient and DSC on locally‐advanced NSCLC patients.