ArticlePhysics and imaging in radiation oncology2025
Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures.
Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- The Evolving Role of Artificial Intelligence in Andrological Surgery: Current Landscape and Future Direction.Journal of clinical medicine · 2026Review
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Authors and funding
20 authors.
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Abstract
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 mm 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.
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