Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate Cancer
<b>Objective:</b> The objective of this study was to develop and assess the clinical feasibility of auto-segmentation and auto-planning methodologies for automated radiotherapy in prostate cancer. <b>Methods</b>: A total of 166 patients were used to train a 3D Unet model for...
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2025-06-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/6/620 |
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| author | Sijuan Huang Jingheng Wu Xi Lin Guangyu Wang Ting Song Li Chen Lecheng Jia Qian Cao Ruiqi Liu Yang Liu Xin Yang Xiaoyan Huang Liru He |
| author_facet | Sijuan Huang Jingheng Wu Xi Lin Guangyu Wang Ting Song Li Chen Lecheng Jia Qian Cao Ruiqi Liu Yang Liu Xin Yang Xiaoyan Huang Liru He |
| author_sort | Sijuan Huang |
| collection | DOAJ |
| description | <b>Objective:</b> The objective of this study was to develop and assess the clinical feasibility of auto-segmentation and auto-planning methodologies for automated radiotherapy in prostate cancer. <b>Methods</b>: A total of 166 patients were used to train a 3D Unet model for segmentation of the gross tumor volume (GTV), clinical tumor volume (CTV), nodal CTV (CTVnd), and organs at risk (OARs). Performance was assessed by the Dice similarity coefficient (<i>DSC</i>), the <i>Recall</i>, <i>Precision</i>, <i>Volume Ratio (VR)</i>, the 95% Hausdorff distance (<i>HD95</i>%), and the volumetric revision degree (<i>VRD</i>). An auto-planning network based on a 3D Unet was trained on 77 treatment plans derived from the 166 patients. Dosimetric differences and clinical acceptability of the auto-plans were studied. The effect of OAR editing on dosimetry was also evaluated. <b>Results</b>: On an independent set of 50 cases, the auto-segmentation process took 1 min 20 s per case. The <i>DSCs</i> for GTV, CTV, and CTVnd were 0.87, 0.88, and 0.82, respectively, with <i>VRD</i>s ranging from 0.09 to 0.14. The segmentation of OARs demonstrated high accuracy (<i>DSC</i> ≥ 0.83, <i>Recall/Precision</i> ≈ 1.0). The auto-planning process required 1–3 optimization iterations for 50%, 40%, and 10% of cases, respectively, and exhibited significant better conformity (<i>p</i> ≤ 0.01) and OAR sparing (<i>p</i> ≤ 0.03) while maintaining comparable target coverage. Only 6.7% of auto-plans were deemed unacceptable compared to 20% of manual plans, with 75% of auto-plans considered superior. Notably, the editing of OARs had no significant impact on doses. <b>Conclusions:</b> The accuracy of auto-segmentation is comparable to that of manual segmentation, and the auto-planning offers equivalent or better OAR protection, meeting the requirements of online automated radiotherapy and facilitating its clinical application. |
| format | Article |
| id | doaj-art-8a62f3414883418d8a43bf0cfd151a32 |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-8a62f3414883418d8a43bf0cfd151a322025-08-20T02:24:22ZengMDPI AGBioengineering2306-53542025-06-0112662010.3390/bioengineering12060620Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate CancerSijuan Huang0Jingheng Wu1Xi Lin2Guangyu Wang3Ting Song4Li Chen5Lecheng Jia6Qian Cao7Ruiqi Liu8Yang Liu9Xin Yang10Xiaoyan Huang11Liru He12Sun Yat-sen University Cancer Center, Guangzhou 510060, ChinaSun Yat-sen University Cancer Center, Guangzhou 510060, ChinaSun Yat-sen University Cancer Center, Guangzhou 510060, ChinaSun Yat-sen University Cancer Center, Guangzhou 510060, ChinaSchool of Biomedical Engineering, Southern Medical University, Guangzhou 510515, ChinaSun Yat-sen University Cancer Center, Guangzhou 510060, ChinaShanghai United Imaging Healthcare Co., Ltd., Shanghai 201807, ChinaShanghai United Imaging Healthcare Co., Ltd., Shanghai 201807, ChinaSun Yat-sen University Cancer Center, Guangzhou 510060, ChinaSun Yat-sen University Cancer Center, Guangzhou 510060, ChinaSun Yat-sen University Cancer Center, Guangzhou 510060, ChinaSun Yat-sen University Cancer Center, Guangzhou 510060, ChinaSun Yat-sen University Cancer Center, Guangzhou 510060, China<b>Objective:</b> The objective of this study was to develop and assess the clinical feasibility of auto-segmentation and auto-planning methodologies for automated radiotherapy in prostate cancer. <b>Methods</b>: A total of 166 patients were used to train a 3D Unet model for segmentation of the gross tumor volume (GTV), clinical tumor volume (CTV), nodal CTV (CTVnd), and organs at risk (OARs). Performance was assessed by the Dice similarity coefficient (<i>DSC</i>), the <i>Recall</i>, <i>Precision</i>, <i>Volume Ratio (VR)</i>, the 95% Hausdorff distance (<i>HD95</i>%), and the volumetric revision degree (<i>VRD</i>). An auto-planning network based on a 3D Unet was trained on 77 treatment plans derived from the 166 patients. Dosimetric differences and clinical acceptability of the auto-plans were studied. The effect of OAR editing on dosimetry was also evaluated. <b>Results</b>: On an independent set of 50 cases, the auto-segmentation process took 1 min 20 s per case. The <i>DSCs</i> for GTV, CTV, and CTVnd were 0.87, 0.88, and 0.82, respectively, with <i>VRD</i>s ranging from 0.09 to 0.14. The segmentation of OARs demonstrated high accuracy (<i>DSC</i> ≥ 0.83, <i>Recall/Precision</i> ≈ 1.0). The auto-planning process required 1–3 optimization iterations for 50%, 40%, and 10% of cases, respectively, and exhibited significant better conformity (<i>p</i> ≤ 0.01) and OAR sparing (<i>p</i> ≤ 0.03) while maintaining comparable target coverage. Only 6.7% of auto-plans were deemed unacceptable compared to 20% of manual plans, with 75% of auto-plans considered superior. Notably, the editing of OARs had no significant impact on doses. <b>Conclusions:</b> The accuracy of auto-segmentation is comparable to that of manual segmentation, and the auto-planning offers equivalent or better OAR protection, meeting the requirements of online automated radiotherapy and facilitating its clinical application.https://www.mdpi.com/2306-5354/12/6/620auto-segmentationauto-planningautomated radiotherapyprostate cancer |
| spellingShingle | Sijuan Huang Jingheng Wu Xi Lin Guangyu Wang Ting Song Li Chen Lecheng Jia Qian Cao Ruiqi Liu Yang Liu Xin Yang Xiaoyan Huang Liru He Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate Cancer Bioengineering auto-segmentation auto-planning automated radiotherapy prostate cancer |
| title | Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate Cancer |
| title_full | Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate Cancer |
| title_fullStr | Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate Cancer |
| title_full_unstemmed | Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate Cancer |
| title_short | Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate Cancer |
| title_sort | auto segmentation and auto planning in automated radiotherapy for prostate cancer |
| topic | auto-segmentation auto-planning automated radiotherapy prostate cancer |
| url | https://www.mdpi.com/2306-5354/12/6/620 |
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