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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Elsevier
2025-07-01
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| Series: | Physics and Imaging in Radiation Oncology |
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| author | 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 |
| author_facet | 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 |
| author_sort | Philipp Schubert |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6200dfb0fd02413093f1eb45bb0e1940 |
| institution | Kabale University |
| issn | 2405-6316 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj-art-6200dfb0fd02413093f1eb45bb0e19402025-08-24T05:13:29ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162025-07-013510082510.1016/j.phro.2025.100825Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structuresPhilipp Schubert0Matthias May1Daniel Höfler2Hans-Peter Fautz3Jana Hutter4Ricarda Merten5Sina Mansoorian6Thomas Weissmann7Lisa Deloch8Miriam Schonath9Nathalia Belmas10Felix Grabenbauer11Benjamin Frey12Udo Gaipl13Bernd-Niklas Axer14Juliane Szkitsak15Michael Uder16Christoph Bert17Rainer Fietkau18Florian Putz19Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany; Corresponding author at: Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitätsstraße 27, D-91054 Erlangen, Germany.CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany; Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyImaging Science Institute Erlangen, Siemens Healthineers, Ulmenweg 18, 91054 Erlangen, Germany; Siemens Healthineers, Siemens Healthineers AG, Siemensstr. 3, 91301 Forchheim, GermanyCCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany; Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyBZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany; Department of Radiation Oncology, University Hospital, Ludwig Maximilian University of Munich 81377 Munich, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany; Siemens Healthineers, Siemens Healthineers AG, Siemensstr. 3, 91301 Forchheim, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyCCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany; Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; CCC Erlangen-EMN, Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; CCC WERA: Comprehensive Cancer Center Alliance WERA (CCC WERA), Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, GermanyBackground 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.http://www.sciencedirect.com/science/article/pii/S2405631625001307Prostate cancerRadiotherapySBRTMR-guidedDeep learningNeurovascular structures |
| spellingShingle | 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 Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures Physics and Imaging in Radiation Oncology Prostate cancer Radiotherapy SBRT MR-guided Deep learning Neurovascular structures |
| title | Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures |
| title_full | Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures |
| title_fullStr | Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures |
| title_full_unstemmed | Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures |
| title_short | Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures |
| title_sort | advancing offline magnetic resonance guided prostate radiotherapy through dedicated imaging and deep learning based automatic contouring of targets and neurovascular structures |
| topic | Prostate cancer Radiotherapy SBRT MR-guided Deep learning Neurovascular structures |
| url | http://www.sciencedirect.com/science/article/pii/S2405631625001307 |
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