Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans. (July 2022)

• Record Type: Journal Article
• Authors: Walls, Gerard M.; Giacometti, Valentina; Apte, Aditya; Thor, Maria; McCann, Conor; Hanna, Gerard G.; O'Connor, John; Deasy, Joseph O.; Hounsell, Alan R.; Butterworth, Karl T.; Cole, Aidan J.; Jain, Suneil; McGarry, Conor K.
• Is Part Of: Physics and imaging in radiation oncology. Volume 23(2022)
• Page Start: 118
• Page End: 126
• Publication Date: 2022-07
• Subjects: Auto-segmentation -- Deep learning -- Cardiac substructures -- Radiotherapy -- Cardiotoxicity -- Lung cancer; Radiotherapy -- Periodicals; Radiation dosimetry -- Periodicals; Cancer -- Imaging -- Periodicals; Oncology -- Periodicals; 615.842
• DOI: 10.1016/j.phro.2022.07.003
• Languages: English
• ISSNs: 2405-6316

Abstract

Highlights: Cardiotoxicity is a common complication of lung cancer radiotherapy. Segmentation of cardiac substructures is time-consuming and challenging. Deep learning segmentation tools can perform this task in 3D and 4D scans. Performance is high when assessed geometrically, dosimetrically and clinically. Auto-segmentation tools may accelerate clinical workflows and enable research. Abstract: Background: Emerging data suggest that dose-sparing several key cardiac regions is prognostically beneficial in lung cancer radiotherapy. The cardiac substructures are challenging to contour due to their complex geometry, poor soft tissue definition on computed tomography (CT) and cardiorespiratory motion artefact. A neural network was previously trained to generate the cardiac substructures using three-dimensional radiotherapy planning CT scans (3D-CT). In this study, the performance of that tool on the average intensity projection from four-dimensional (4D) CT scans (4D-AVE), now commonly used in lung radiotherapy, was evaluated. Materials and Methods: The 4D-AVE of n=20 patients completing radiotherapy for lung cancer 2015–2020 underwent manual and automated cardiac substructure segmentation. Manual and automated substructures were compared geometrically and dosimetrically. Two senior clinicians also qualitatively assessed the auto-segmentation tool's output. Results: Geometric comparison of the automated and manual segmentations exhibited high levels of similarity across parameters, including volume difference (11.8% overall) and Dice similarity coefficient (0.85 overall), and were consistent with 3D-CT performance. Differences in mean (median 0.2 Gy, range −1.6–0.3 Gy) and maximum (median 0.4 Gy, range −2.2–0.9 Gy) doses to substructures were generally small. Nearly all structures (99.5 %) were deemed to be appropriate for clinical use without further editing. Conclusions: Cardiac substructure auto-segmentation using a deep learning-based tool trained on a 3D-CT dataset was feasible on the 4D-AVE scan, meaning this tool is suitable for use on 4D-CT radiotherapy planning scans. Application of this tool would increase the practicality of routine clinical cardiac substructure delineation, and enable further cardiac radiation effects research.