Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review
As yet, there is no systematic review focusing on benefits and issues of commercial deep learning-based auto-segmentation (DLAS) software for prostate cancer (PCa) radiation therapy (RT) planning despite that NRG Oncology has underscored such necessity. This article’s purpose is to systematically re...
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| Format: | Journal Article |
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MDPI
2025
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| Online Access: | https://www.mdpi.com/2078-2489/16/3/215 http://hdl.handle.net/20.500.11937/97316 |
| _version_ | 1848766253315915776 |
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| author | Ng, Curtise |
| author_facet | Ng, Curtise |
| author_sort | Ng, Curtise |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | As yet, there is no systematic review focusing on benefits and issues of commercial deep learning-based auto-segmentation (DLAS) software for prostate cancer (PCa) radiation therapy (RT) planning despite that NRG Oncology has underscored such necessity. This article’s purpose is to systematically review commercial DLAS software product performances for PCa RT planning and their associated evaluation methodology. A literature search was performed with the use of electronic databases on 7 November 2024. Thirty-two articles were included as per the selection criteria. They evaluated 12 products (Carina Medical LLC INTContour (Lexington, KY, USA), Elekta AB ADMIRE (Stockholm, Sweden), Limbus AI Inc. Contour (Regina, SK, Canada), Manteia Medical Technologies Co. AccuContour (Jian Sheng, China), MIM Software Inc. Contour ProtégéAI (Cleveland, OH, USA), Mirada Medical Ltd. DLCExpert (Oxford, UK), MVision.ai Contour+ (Helsinki, Finland), Radformation Inc. AutoContour (New York, NY, USA), RaySearch Laboratories AB RayStation (Stockholm, Sweden), Siemens Healthineers AG AI-Rad Companion Organs RT, syngo.via RT Image Suite and DirectORGANS (Erlangen, Germany), Therapanacea Annotate (Paris, France), and Varian Medical Systems, Inc. Ethos (Palo Alto, CA, USA)). Their results illustrate that the DLAS products can delineate 12 organs at risk (abdominopelvic cavity, anal canal, bladder, body, cauda equina, left (L) and right (R) femurs, L and R pelvis, L and R proximal femurs, and sacrum) and four clinical target volumes (prostate, lymph nodes, prostate bed, and seminal vesicle bed) with clinically acceptable outcomes, resulting in delineation time reduction, 5.7–81.1%. Although NRG Oncology has recommended each clinical centre to perform its own DLAS product evaluation prior to clinical implementation, such evaluation seems more important for AccuContour and Ethos due to the methodological issues of the respective single studies, e.g., small dataset used, etc. |
| first_indexed | 2025-11-14T11:48:12Z |
| format | Journal Article |
| id | curtin-20.500.11937-97316 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:48:12Z |
| publishDate | 2025 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-973162025-05-20T01:23:11Z Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review Ng, Curtise Artificial Intelligence Artificial Neural Network Automatic Clinical Target Volumes Computed Tomography Contouring Delineation Machine Learning Organs at Risk Radiotherapy As yet, there is no systematic review focusing on benefits and issues of commercial deep learning-based auto-segmentation (DLAS) software for prostate cancer (PCa) radiation therapy (RT) planning despite that NRG Oncology has underscored such necessity. This article’s purpose is to systematically review commercial DLAS software product performances for PCa RT planning and their associated evaluation methodology. A literature search was performed with the use of electronic databases on 7 November 2024. Thirty-two articles were included as per the selection criteria. They evaluated 12 products (Carina Medical LLC INTContour (Lexington, KY, USA), Elekta AB ADMIRE (Stockholm, Sweden), Limbus AI Inc. Contour (Regina, SK, Canada), Manteia Medical Technologies Co. AccuContour (Jian Sheng, China), MIM Software Inc. Contour ProtégéAI (Cleveland, OH, USA), Mirada Medical Ltd. DLCExpert (Oxford, UK), MVision.ai Contour+ (Helsinki, Finland), Radformation Inc. AutoContour (New York, NY, USA), RaySearch Laboratories AB RayStation (Stockholm, Sweden), Siemens Healthineers AG AI-Rad Companion Organs RT, syngo.via RT Image Suite and DirectORGANS (Erlangen, Germany), Therapanacea Annotate (Paris, France), and Varian Medical Systems, Inc. Ethos (Palo Alto, CA, USA)). Their results illustrate that the DLAS products can delineate 12 organs at risk (abdominopelvic cavity, anal canal, bladder, body, cauda equina, left (L) and right (R) femurs, L and R pelvis, L and R proximal femurs, and sacrum) and four clinical target volumes (prostate, lymph nodes, prostate bed, and seminal vesicle bed) with clinically acceptable outcomes, resulting in delineation time reduction, 5.7–81.1%. Although NRG Oncology has recommended each clinical centre to perform its own DLAS product evaluation prior to clinical implementation, such evaluation seems more important for AccuContour and Ethos due to the methodological issues of the respective single studies, e.g., small dataset used, etc. 2025 Journal Article http://hdl.handle.net/20.500.11937/97316 10.3390/info16030215 https://www.mdpi.com/2078-2489/16/3/215 https://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Artificial Intelligence Artificial Neural Network Automatic Clinical Target Volumes Computed Tomography Contouring Delineation Machine Learning Organs at Risk Radiotherapy Ng, Curtise Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review |
| title | Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review |
| title_full | Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review |
| title_fullStr | Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review |
| title_full_unstemmed | Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review |
| title_short | Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review |
| title_sort | performance of commercial deep learning-based auto-segmentation software for prostate cancer radiation therapy planning: a systematic review |
| topic | Artificial Intelligence Artificial Neural Network Automatic Clinical Target Volumes Computed Tomography Contouring Delineation Machine Learning Organs at Risk Radiotherapy |
| url | https://www.mdpi.com/2078-2489/16/3/215 http://hdl.handle.net/20.500.11937/97316 |