Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis
Background: Radiotherapy (RT) is a mainstay treatment for prostate cancer (PC). Accurate delineation of organs at risk (OARs) is crucial for optimizing the therapeutic window by minimizing side effects. Manual segmentation is time-consuming and prone to inter-operator variability. This study investi...
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| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Current Oncology |
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| Online Access: | https://www.mdpi.com/1718-7729/32/6/321 |
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| author | Cristiano Grossi Fernando Munoz Ilaria Bonavero Eulalie Joelle Tondji Ngassam Elisabetta Garibaldi Claudia Airaldi Elena Celia Daniela Nassisi Andrea Brignoli Elisabetta Trino Lavinia Bianco Silvia Leardi Diego Bongiovanni Chiara Valero Maria Grazia Ruo Redda |
| author_facet | Cristiano Grossi Fernando Munoz Ilaria Bonavero Eulalie Joelle Tondji Ngassam Elisabetta Garibaldi Claudia Airaldi Elena Celia Daniela Nassisi Andrea Brignoli Elisabetta Trino Lavinia Bianco Silvia Leardi Diego Bongiovanni Chiara Valero Maria Grazia Ruo Redda |
| author_sort | Cristiano Grossi |
| collection | DOAJ |
| description | Background: Radiotherapy (RT) is a mainstay treatment for prostate cancer (PC). Accurate delineation of organs at risk (OARs) is crucial for optimizing the therapeutic window by minimizing side effects. Manual segmentation is time-consuming and prone to inter-operator variability. This study investigates the performance of Limbus<sup>®</sup> Contour<sup>®</sup> (LC), a deep learning-based auto-contouring software, in delineating pelvic structures in PC patients. Methods: We evaluated LC’s performance on key structures (bowel bag, bladder, rectum, sigmoid colon, and pelvic lymph nodes) in 52 patients. We compared auto-contoured structures with those manually delineated by radiation oncologists using different metrics. Results: LC achieved good agreement for the bladder (median Dice: 0.95) and rectum (median Dice: 0.83). However, limitations were observed for the bowel bag (median Dice: 0.64) and sigmoid colon (median Dice: 0.6), with inclusion of irrelevant structures. While the median Dice for pelvic lymph nodes was acceptable (0.73), the software lacked sub-regional differentiation, limiting its applicability in certain other oncologic settings. Conclusions: LC shows promise for automating OAR delineation in prostate radiotherapy, particularly for the bladder and rectum. Improvements are needed for bowel bag, sigmoid colon, and lymph node sub-regionalization. Further validation with a broader and larger patient cohort is recommended to assess generalizability. |
| format | Article |
| id | doaj-art-bdb7896c72a34e2b828fd6d61957d4a8 |
| institution | Kabale University |
| issn | 1198-0052 1718-7729 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Current Oncology |
| spelling | doaj-art-bdb7896c72a34e2b828fd6d61957d4a82025-08-20T03:24:32ZengMDPI AGCurrent Oncology1198-00521718-77292025-05-0132632110.3390/curroncol32060321Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric AnalysisCristiano Grossi0Fernando Munoz1Ilaria Bonavero2Eulalie Joelle Tondji Ngassam3Elisabetta Garibaldi4Claudia Airaldi5Elena Celia6Daniela Nassisi7Andrea Brignoli8Elisabetta Trino9Lavinia Bianco10Silvia Leardi11Diego Bongiovanni12Chiara Valero13Maria Grazia Ruo Redda14Department of Oncology, University of Turin School of Medicine, 10126 Turin, ItalyDepartment of Radiation Oncology, Umberto Parini Hospital, 11100 Aosta, ItalyDepartment of Radiation Oncology, Umberto Parini Hospital, 11100 Aosta, ItalyDepartment of Oncology, University of Turin School of Medicine, 10126 Turin, ItalyDepartment of Radiation Oncology, Umberto Parini Hospital, 11100 Aosta, ItalyDepartment of Radiation Oncology, Mauriziano Umberto I Hospital, 10128 Turin, ItalyDepartment of Radiation Oncology, Umberto Parini Hospital, 11100 Aosta, ItalyDepartment of Radiation Oncology, Mauriziano Umberto I Hospital, 10128 Turin, ItalyDepartment of Radiation Oncology, Umberto Parini Hospital, 11100 Aosta, ItalyDepartment of Radiation Oncology, Mauriziano Umberto I Hospital, 10128 Turin, ItalyDepartment of Radiation Oncology, Mauriziano Umberto I Hospital, 10128 Turin, ItalyDepartment of Radiation Oncology, Mauriziano Umberto I Hospital, 10128 Turin, ItalyDepartment of Radiation Oncology, Mauriziano Umberto I Hospital, 10128 Turin, ItalyDepartment of Medical Physics, Mauriziano Umberto I Hospital, 10128 Turin, ItalyDepartment Oncology, University of Turin School of Medicine, Mauriziano Umberto I Hospital, 10128 Turin, ItalyBackground: Radiotherapy (RT) is a mainstay treatment for prostate cancer (PC). Accurate delineation of organs at risk (OARs) is crucial for optimizing the therapeutic window by minimizing side effects. Manual segmentation is time-consuming and prone to inter-operator variability. This study investigates the performance of Limbus<sup>®</sup> Contour<sup>®</sup> (LC), a deep learning-based auto-contouring software, in delineating pelvic structures in PC patients. Methods: We evaluated LC’s performance on key structures (bowel bag, bladder, rectum, sigmoid colon, and pelvic lymph nodes) in 52 patients. We compared auto-contoured structures with those manually delineated by radiation oncologists using different metrics. Results: LC achieved good agreement for the bladder (median Dice: 0.95) and rectum (median Dice: 0.83). However, limitations were observed for the bowel bag (median Dice: 0.64) and sigmoid colon (median Dice: 0.6), with inclusion of irrelevant structures. While the median Dice for pelvic lymph nodes was acceptable (0.73), the software lacked sub-regional differentiation, limiting its applicability in certain other oncologic settings. Conclusions: LC shows promise for automating OAR delineation in prostate radiotherapy, particularly for the bladder and rectum. Improvements are needed for bowel bag, sigmoid colon, and lymph node sub-regionalization. Further validation with a broader and larger patient cohort is recommended to assess generalizability.https://www.mdpi.com/1718-7729/32/6/321auto-contouringorgans at riskdeep learningsegmentationprostate cancerpelvic lymph nodes |
| spellingShingle | Cristiano Grossi Fernando Munoz Ilaria Bonavero Eulalie Joelle Tondji Ngassam Elisabetta Garibaldi Claudia Airaldi Elena Celia Daniela Nassisi Andrea Brignoli Elisabetta Trino Lavinia Bianco Silvia Leardi Diego Bongiovanni Chiara Valero Maria Grazia Ruo Redda Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis Current Oncology auto-contouring organs at risk deep learning segmentation prostate cancer pelvic lymph nodes |
| title | Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis |
| title_full | Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis |
| title_fullStr | Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis |
| title_full_unstemmed | Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis |
| title_short | Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis |
| title_sort | can deep learning based auto contouring software achieve accurate pelvic volume delineation in volumetric image guided radiotherapy for prostate cancer a preliminary multicentric analysis |
| topic | auto-contouring organs at risk deep learning segmentation prostate cancer pelvic lymph nodes |
| url | https://www.mdpi.com/1718-7729/32/6/321 |
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