Pulmonary ventilation images from planning CT images using deep learning on locally-advanced NSCLC patients.
Authors
Affiliations (1)
Affiliations (1)
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.
Abstract
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. We developed a framework that generates computed tomography ventilation images (CTVI) from planning CT (PCT) images and demonstrated its estimation performance. 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 ( <math xmlns="http://www.w3.org/1998/Math/MathML"><msubsup><mi>CTVI</mi> <mrow><mn>4</mn> <mi>DCT</mi></mrow> <mi>DIR</mi></msubsup> </math> ) images was also generated using both deformable image registration and the computing methods employed in the VAMPIRE study. The CTVI based on PCT ( <math xmlns="http://www.w3.org/1998/Math/MathML"><msubsup><mi>CTVI</mi> <mi>PCT</mi> <mi>DNN</mi></msubsup> </math> ) estimated by a model trained via hyperparameter optimization of a U-net deep neural network with 5-fold cross-validation was compared with <math xmlns="http://www.w3.org/1998/Math/MathML"><msubsup><mi>CTVI</mi> <mrow><mn>4</mn> <mi>DCT</mi></mrow> <mi>DIR</mi></msubsup> </math> . In 5-fold cross-validation, the average voxel-wise Spearman's correlation coefficient (r<sub>s</sub>) ± one standard deviation between <math xmlns="http://www.w3.org/1998/Math/MathML"><msubsup><mi>CTVI</mi> <mi>PCT</mi> <mi>DNN</mi></msubsup> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML"><msubsup><mi>CTVI</mi> <mrow><mn>4</mn> <mi>DCT</mi></mrow> <mi>DIR</mi></msubsup> </math> 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 DSC<sub>high</sub>, DSC<sub>moderate</sub>, and DSC<sub>low</sub> values of 0.68 ± 0.06, 0.51 ± 0.08, and 0.75 ± 0.04, respectively. We successfully developed a framework that estimates <math xmlns="http://www.w3.org/1998/Math/MathML"><msubsup><mi>CTVI</mi> <mi>PCT</mi> <mi>DNN</mi></msubsup> </math> , and demonstrated its estimation performance in terms of Spearman's correlation coefficient and DSC on locally-advanced NSCLC patients.