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

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology Pub Date : 2025-07-01 DOI:10.1016/j.phro.2025.100825

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

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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 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.

Abstract Image

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通过专用成像和基于深度学习的目标和神经血管结构自动轮廓，推进离线磁共振引导前列腺放疗

背景与目的勃起功能障碍（ED）影响前列腺癌放疗后的生活质量。与计算机断层扫描（CT）相比，磁共振成像（MRI）规划提供了更好的与电位相关的解剖结构的可视化，从而提高了节约。然而，在临床实践中塑造这些结构是费时的，需要专业知识。MRI模拟的深度学习（DL）自动轮廓可以使神经血管保留放射治疗更容易获得。材料与方法对50例患者在治疗体位获得高分辨率三维t2加权SPACE MRI序列（<；1 mm3分辨率）。一位泌尿放射肿瘤学专家绘制了与勃起功能相关的解剖图（神经血管束[NVB]、阴部动脉[IPA]、阴茎球[PB]、海绵体[CC]）和靶结构（前列腺[PR]、精囊[SV]）。训练了40个数据集，其中10个测试了一个3D nnU-net模型。对dl生成的轮廓进行几何评估（表面Dice Score [sDSC]，平均表面距离[MSD]），并通过盲法专家评审进行验证。结果dl自动分割的平均sDSC为0.82 (IPA: 0.93, NVB: 0.71, PB: 0.84, CC: 0.90, PR: 0.74, SV: 0.79)，平均MSD为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）。除阴部动脉外，盲法评分显示DL和专家轮廓之间无显著差异（平均DL与专家；NVB: 82对85；PB: 86对88；CC: 83对88；PR 81对83；SV 78对81，均p >； 0.05； IPA: 82对89；p = 0.028）。将高分辨率MRI模拟与DL后处理相结合，可以为mr引导下的SBRT规划提供准确的自动轮廓，有可能使神经血管保留放疗超越当前标准。

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