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"Deep learning improves robustness of contour propagation for online adaptive IMPT of prostate cancer"

Mohamed S. Elmahdy, Thyrza Jagt, R. Th. Zinkstok, C.A.M. Marijnen, Mischa Hoogeman and Marius Staring

Abstract

Purpose/objective: Online-adaptive radiotherapy holds the promise to mitigate daily anatomical uncertainties thereby increasing treatment precision. One of the major challenges here is the development and validation of sufficiently fast, accurate, and robust segmentation algorithms for target volumes and organs at risk. The purpose of this study is to improve contour propagation in the pelvic region by combining deep-learning based auto-segmentation with image registration and to validate it geometrically and dosimetrically for online-adaptive Proton Therapy (PT) for prostate cancer.

Material and Methods: The proposed registration pipeline registers the daily CT scan to the planning, based on a combination of image intensities and, moreover, by an automatic segmentation of the bladder from the daily CT scan using a novel 3D convolutional neural network. The bladder was chosen for its influence on prostate motion. Registration performance is further enhanced by a digital inpainting of gas pockets with realistic bowel content, using a state-of-the-art generative adversarial network. We further perform data normalization and in relevant cases digitally remove contrast agent from the bladder.
Evaluation was performed on CT data from 18 prostate cancer patients, each with 7 to 10 repeat CT scans. Manual delineations of the prostate, lymph nodes, seminal vesicles, bladder and rectum were available for evaluation. Geometric performance was quantified using the Mean Surface Distance (MSD). The pipeline was validated dosimetrically on 11 out of 18 patients by simulating an online-adaptive PT workflow based on the propagated contours. To this end, for each repeat CT, a treatment plan was generated based on the propagated contours and the plan was evaluated using the manual delineations. A dose of 74 Gy was assigned to the high-dose PTV (prostate) and 55 Gy to the low-dose PTV (lymph nodes and seminal vesicles). The generated treatment plans were considered clinically acceptable if dosimetric coverage constraints derived from the manual contours were met (PTV V95% = 98% and V107% = 2%).

Results: The proposed pipeline achieved a MSD of 1.29 0.33, 1.44 0.68, and 1.52 0.45 mm for the prostate, seminal vesicles, and lymph nodes, respectively (Fig. 1). The propagated contours met the dose coverage constraints in 85%, 91%, and 99% of the cases for the prostate, seminal vesicles, and lymph nodes, respectively (Fig. 2). 78% of the cases met all constraints at the same time, compared to 65% when using a standard registration approach. The average runtime for the proposed pipeline is 98 seconds per registration.

Conclusion: The proposed registration pipeline obtained highly promising results for generating treatment plans adapted to the daily anatomy. With 78% of the automatically generated treatment plans directly usable without manual correction, a substantial improvement in system robustness was reached compared to an existing approach. The proposed method therefore facilitates more precise PT of prostate cancer.

 

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Copyright © 2019 by the authors. Published version © 2019 by . Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@article{Elmahdy:2019,
author = "{Mohamed S. Elmahdy and Thyrza Jagt and R. Th. Zinkstok and C.A.M. Marijnen and Mischa Hoogeman and Marius Staring}",
title = "{Deep learning improves robustness of contour propagation for online adaptive IMPT of prostate cancer}",
journal = "{ESTRO}",
month = "{April}",
year = "{2019}",
}

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