Systematic Evaluation of the Impact of Lung Segmentation Methods on 4-Dimensional Computed Tomography Ventilation Imaging Using a Large Patient Database.

Authors

• Yingxuan Chen
• Soroush Heidari Pahlavian
• Paul Jacobs
• Taindra Neupane
• Farnoush Forghani-Arani
• Edward Castillo
• Richard Castillo
• Yevgeniy Vinogradskiy

Publication/Presentation Date

1-1-2024

Abstract

PURPOSE: A novel form of lung functional imaging applied for functional avoidance radiation therapy has been developed that uses 4-dimensional computed tomography (4DCT) data and image processing techniques to calculate lung ventilation (4DCT-ventilation). Lung segmentation is a common step to define a region of interest for 4DCT-ventilation generation. The purpose of this study was to quantitatively evaluate the sensitivity of 4DCT-ventilation imaging using different lung segmentation methods.

METHODS AND MATERIALS: The 4DCT data of 350 patients from 2 institutions were used. Lung contours were generated using 3 methods: (1) reference segmentations that removed airways and pulmonary vasculature manually (Lung-Manual), (2) standard lung contours used for planning (Lung-RadOnc), and (3) artificial intelligence (AI)-based contours that removed the airways and pulmonary vasculature (Lung-AI). The AI model was based on a residual 3-dimensional U-Net and was trained using the Lung-Manual contours of 279 patients. We compared the Lung-RadOnc or Lung-AI with Lung-Manual contours for the entire 4DCT-ventilation functional avoidance process including lung segmentation (surface Dice similarity coefficient [Surface DSC]), 4DCT-ventilation generation (correlation), and subanalysis of 10 patients on a dosimetric endpoint (percentage of high functional volume of lung receiving ≥20 Gy [fV20{%}]).

RESULTS: Surface DSC comparing Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI contours was 0.40 ± 0.06 and 0.86 ± 0.04, respectively. The correlation between 4DCT-ventilation images generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI were 0.48 ± 0.14 and 0.85 ± 0.14, respectively. The difference in fV20[%] between 4DCT-ventilation generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI was 2.5% ± 4.1% and 0.3% ± 0.5%, respectively.

CONCLUSIONS: Our work showed that using standard planning lung contours can result in significantly variable 4DCT-ventilation images. The study demonstrated that AI-based segmentations generate lung contours and 4DCT-ventilation images that are similar to those generated using manual methods. The significance of the study is that it characterizes the lung segmentation sensitivity of the 4DCT-ventilation process and develops methods that can facilitate the integration of this novel imaging in busy clinics.

Volume

118

Issue

1

First Page

242

Last Page

252

ISSN

1879-355X

Published In/Presented At

Chen, Y., Pahlavian, S. H., Jacobs, P., Neupane, T., Forghani-Arani, F., Castillo, E., Castillo, R., & Vinogradskiy, Y. (2024). Systematic Evaluation of the Impact of Lung Segmentation Methods on 4-Dimensional Computed Tomography Ventilation Imaging Using a Large Patient Database. International journal of radiation oncology, biology, physics, 118(1), 242–252. https://doi.org/10.1016/j.ijrobp.2023.08.017

Disciplines

Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods

PubMedID

37607642

Department(s)

Administration and Leadership

Document Type

Article