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

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 ( CTVI 4 DCT DIR ) images was also generated using both deformable image registration and the computing methods employed in the VAMPIRE study.

RESULTS

The CTVI based on PCT ( CTVI PCT DNN ) estimated by a model trained via hyperparameter optimization of a U-net deep neural network with 5-fold cross-validation was compared with CTVI 4 DCT DIR . In 5-fold cross-validation, the average voxel-wise Spearman's correlation coefficient (rs) ± one standard deviation between CTVI PCT DNN and CTVI 4 DCT DIR 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 CTVI PCT DNN , and demonstrated its estimation performance in terms of Spearman's correlation coefficient and DSC on locally-advanced NSCLC patients.