Y Chen¹*, S Pahlavian², P Jacobs², F Forghani³, E Castillo⁴, R Castillo⁵, Y Vinogradskiy¹, (1) Thomas Jefferson University, Philadelphia, PA, (2) MIM Software Inc., Beachwood, OH, (3) Washington University, Creve Coeur, MO, (4) University of Texas at Austin, Austin, TX, (5) Emory University, Atlanta, GA

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  Y Chen

WE-C1000-IePD-F1-3 (Wednesday, 7/13/2022) 10:00 AM - 10:30 AM [Eastern Time (GMT-4)]

Exhibit Hall | Forum 1

Purpose: Functional avoidance radiotherapy uses functional imaging to reduce pulmonary toxicity by designing radiotherapy plans that reduce doses to functional lung. A novel form of lung functional imaging applied for functional avoidance uses 4DCT imaging to calculating 4DCT-based lung ventilation (4DCT-ventilation). The process of generating 4DCT-ventilation images requires advanced lung segmentation (consisting of a standard lung with airway and vasculature removed) which is a manual and time-consuming process. The purpose of this work is to automate 4DCT-ventilation imaging generation using AI-based auto-segmentation techniques for advanced lung contouring.

Methods: 429 patients with 4DCT data from two institutions were used. Three methods for lung contours were generated including two advanced (with airway and vasculature removed): 1) manual segmentation (‘Lung-Manual’), 2) AI-based contours (‘Lung-AI’) and 3) AI-based standard lung contours used for treatment planning (‘Lung-RadOnc’). The AI model based on a residual 3D U-Net was trained using Lung-Manual of 356 patients. The predicted Lung-AI were validated against Lung-Manual contours using 73 independent patients with Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). 4DCT-ventilaton images were calculated using all three contours and the images generated with the Lung-RadOnc and Lung-AI contours were compared against images generated with Lung-Manual contours (Pearson correlation).

Results: The DSC and HD95 comparing Lung-AI and Lung-Manual contours were 0.95±0.02 and 3.61±15.97 mm, respectively. The correlation between 4DCT-ventilation images generated with Lung-AI and Lung-Manual contours was 0.83±0.17, while the correlation between Lung-RadOnc and Lung-Manual-based imaging was 0.48±0.14.

Conclusion: Our study shows that using standard lung contours can result in inaccurate 4DCT-ventlation images and using AI-based advanced lung contours can produce 4DCT-ventilation images highly-correlated to those generated using manual (and time consuming) methods. The presented study uses a large patient database to automate the 4DCT-ventilation image generation process, which facilitates the integration of this novel imaging modality into busy clinical environments.

Funding Support, Disclosures, and Conflict of Interest: Funded by NCI RO1CA236857 and research agreement with MIM software

Keywords

Lung, Ventilation/perfusion, Functional Imaging

Taxonomy

IM/TH- Image Analysis (Single Modality or Multi-Modality): Quantitative imaging

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