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

Authors

Yoshiyuki Katsuta, Taichi Hoshino, Takaya Yamamoto, Noriyuki Kadoya, Shohei Tanaka, Kazuhiro Arai, Rei Umezawa, Noriyoshi Takahashi, Keiichi Jingu

Published in

Journal of applied clinical medical physics. Volume 27. Issue 9. Pages e70777.

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 ( ${\mathrm{CTVI}}_{4\mathrm{DCT}}^{\mathrm{DIR}}$ ) images was also generated using both deformable image registration and the computing methods employed in the VAMPIRE study.
The CTVI based on PCT ( ${\mathrm{CTVI}}_{\mathrm{PCT}}^{\mathrm{DNN}}$ ) estimated by a model trained via hyperparameter optimization of a U-net deep neural network with 5-fold cross-validation was compared with ${\mathrm{CTVI}}_{4\mathrm{DCT}}^{\mathrm{DIR}}$ . In 5-fold cross-validation, the average voxel-wise Spearman's correlation coefficient (r_s) ± one standard deviation between ${\mathrm{CTVI}}_{\mathrm{PCT}}^{\mathrm{DNN}}$ and ${\mathrm{CTVI}}_{4\mathrm{DCT}}^{\mathrm{DIR}}$ 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 DSC_high, DSC_moderate, and DSC_low values of 0.68 ± 0.06, 0.51 ± 0.08, and 0.75 ± 0.04, respectively.
We successfully developed a framework that estimates ${\mathrm{CTVI}}_{\mathrm{PCT}}^{\mathrm{DNN}}$ , and demonstrated its estimation performance in terms of Spearman's correlation coefficient and DSC on locally-advanced NSCLC patients.

PMID:
42665930
Bibliographic data and abstract were imported from PubMed on 29 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.