Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures

Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures

Background and Purpose: Erectile dysfunction (ED) affects quality of life following radiotherapy for prostate cancer. Magnetic resonance imaging (MRI) planning provides superior visualization of potency-related anatomical structures compared to computed tomography (CT), enabling improved sparing. Ho...

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Main Authors: Philipp Schubert, Matthias May, Daniel Höfler, Hans-Peter Fautz, Jana Hutter, Ricarda Merten, Sina Mansoorian, Thomas Weissmann, Lisa Deloch, Miriam Schonath, Nathalia Belmas, Felix Grabenbauer, Benjamin Frey, Udo Gaipl, Bernd-Niklas Axer, Juliane Szkitsak, Michael Uder, Christoph Bert, Rainer Fietkau, Florian Putz
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Prostate cancer
Radiotherapy
SBRT
MR-guided
Deep learning
Neurovascular structures
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631625001307
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Summary:Background and Purpose: Erectile dysfunction (ED) affects quality of life following radiotherapy for prostate cancer. Magnetic resonance imaging (MRI) planning provides superior visualization of potency-related anatomical structures compared to computed tomography (CT), enabling improved sparing. However, contouring these structures in clinical practice is time-intensive and requires expertise. Deep learning (DL) auto-contouring with MRI simulation could make neurovascular-sparing radiotherapy more accessible. Material and Methods: High-resolution 3D T2-weighted SPACE MRI sequences (<1 mm3 resolution) were obtained for 50 patients in treatment position. An expert uro-radiation oncologist contoured erectile function-related anatomy (neurovascular bundles [NVB], pudendal arteries [IPA], penile bulb [PB], corpora cavernosa [CC]) and target structures (prostate [PR], seminal vesicles [SV]). Forty datasets trained and ten tested a 3D nnU-net model. DL-generated contours were geometrically evaluated (surface Dice Score [sDSC], mean surface distance [MSD]) and validated by blinded expert review. Results: DL auto-segmentation achieved an average sDSC of 0.82 (IPA: 0.93, NVB: 0.71, PB: 0.84, CC: 0.90, PR: 0.74; SV: 0.79) and average MSD of 0.74 mm (IPA: 0.61 mm; NVB: 0.88 mm; PB: 0.63 mm; CC: 0.47 mm; PR 0.83 mm; SV: 1.01 mm). Blinded ratings showed no significant differences between DL and expert contours, except for pudendal arteries (Mean DL vs. expert; NVB: 82 vs. 85; PB: 86 vs. 88; CC: 83 vs. 88; PR 81 vs. 83; SV 78 vs. 81 all p > 0.05; IPA: 82 vs. 89; p = 0.028). Conclusion: Combining high-resolution MRI simulation with DL postprocessing enables accurate auto-contouring for MR-guided SBRT planning, potentially advancing neurovascular-sparing radiotherapy beyond current standards.
ISSN:2405-6316

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