Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy
Background and Purpose:: With the increasing amount of in-house created deep learning models in radiotherapy, it is important to know how to minimise the risks associated with the local clinical implementation prior to clinical use. The goal of this study is to give an example of how to identify the...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-10-01
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| Series: | Physics and Imaging in Radiation Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405631624001477 |
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| author | Liesbeth Vandewinckele Chahrazad Benazzouz Laurence Delombaerde Laure Pape Truus Reynders Aline Van der Vorst Dylan Callens Jan Verstraete Adinda Baeten Caroline Weltens Wouter Crijns |
| author_facet | Liesbeth Vandewinckele Chahrazad Benazzouz Laurence Delombaerde Laure Pape Truus Reynders Aline Van der Vorst Dylan Callens Jan Verstraete Adinda Baeten Caroline Weltens Wouter Crijns |
| author_sort | Liesbeth Vandewinckele |
| collection | DOAJ |
| description | Background and Purpose:: With the increasing amount of in-house created deep learning models in radiotherapy, it is important to know how to minimise the risks associated with the local clinical implementation prior to clinical use. The goal of this study is to give an example of how to identify the risks and find mitigation strategies to reduce these risks in an implemented workflow containing a deep learning based planning tool for breast Volumetric Modulated Arc Therapy. Materials and Methods:: The deep learning model ran on a private Google Cloud environment for adequate computational capacity and was integrated into a workflow that could be initiated within the clinical Treatment Planning System (TPS). A proactive Failure Mode and Effect Analysis (FMEA) was conducted by a multidisciplinary team, including physicians, physicists, dosimetrists, technologists, quality managers, and the research and development team. Failure modes categorised as ‘Not acceptable’ and ‘Tolerable’ on the risk matrix were further examined to find mitigation strategies. Results:: In total, 39 failure modes were defined for the total workflow, divided over four steps. Of these, 33 were deemed ‘Acceptable’, five ‘Tolerable’, and one ‘Not acceptable’. Mitigation strategies, such as a case-specific Quality Assurance report, additional scripted checks and properties, a pop-up window, and time stamp analysis, reduced the failure modes to two ‘Tolerable’ and none in the ‘Not acceptable’ region. Conclusions:: The pro-active risk analysis revealed possible risks in the implemented workflow and led to the implementation of mitigation strategies that decreased the risk scores for safer clinical use. |
| format | Article |
| id | doaj-art-c3d53bd7573940e18a230ed0b7c88503 |
| institution | Kabale University |
| issn | 2405-6316 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj-art-c3d53bd7573940e18a230ed0b7c885032024-12-19T10:55:43ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162024-10-0132100677Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc TherapyLiesbeth Vandewinckele0Chahrazad Benazzouz1Laurence Delombaerde2Laure Pape3Truus Reynders4Aline Van der Vorst5Dylan Callens6Jan Verstraete7Adinda Baeten8Caroline Weltens9Wouter Crijns10Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Correspondence to: Laboratory of Experimental Radiotherapy, UZ Herestraat 49 - bus 7003, 3000 Leuven, Belgium.Department of Radiation Oncology, UZ Leuven, BelgiumDepartment of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, BelgiumDepartment of Radiation Oncology, UZ Leuven, BelgiumDepartment of Radiation Oncology, UZ Leuven, BelgiumDepartment of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, BelgiumDepartment of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, BelgiumDepartment of Radiation Oncology, UZ Leuven, BelgiumDepartment of Radiation Oncology, UZ Leuven, BelgiumDepartment of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, BelgiumDepartment of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, BelgiumBackground and Purpose:: With the increasing amount of in-house created deep learning models in radiotherapy, it is important to know how to minimise the risks associated with the local clinical implementation prior to clinical use. The goal of this study is to give an example of how to identify the risks and find mitigation strategies to reduce these risks in an implemented workflow containing a deep learning based planning tool for breast Volumetric Modulated Arc Therapy. Materials and Methods:: The deep learning model ran on a private Google Cloud environment for adequate computational capacity and was integrated into a workflow that could be initiated within the clinical Treatment Planning System (TPS). A proactive Failure Mode and Effect Analysis (FMEA) was conducted by a multidisciplinary team, including physicians, physicists, dosimetrists, technologists, quality managers, and the research and development team. Failure modes categorised as ‘Not acceptable’ and ‘Tolerable’ on the risk matrix were further examined to find mitigation strategies. Results:: In total, 39 failure modes were defined for the total workflow, divided over four steps. Of these, 33 were deemed ‘Acceptable’, five ‘Tolerable’, and one ‘Not acceptable’. Mitigation strategies, such as a case-specific Quality Assurance report, additional scripted checks and properties, a pop-up window, and time stamp analysis, reduced the failure modes to two ‘Tolerable’ and none in the ‘Not acceptable’ region. Conclusions:: The pro-active risk analysis revealed possible risks in the implemented workflow and led to the implementation of mitigation strategies that decreased the risk scores for safer clinical use.http://www.sciencedirect.com/science/article/pii/S2405631624001477Risk analysisFMEAIn-house toolAutomated breast planningDeep learning |
| spellingShingle | Liesbeth Vandewinckele Chahrazad Benazzouz Laurence Delombaerde Laure Pape Truus Reynders Aline Van der Vorst Dylan Callens Jan Verstraete Adinda Baeten Caroline Weltens Wouter Crijns Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy Physics and Imaging in Radiation Oncology Risk analysis FMEA In-house tool Automated breast planning Deep learning |
| title | Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy |
| title_full | Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy |
| title_fullStr | Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy |
| title_full_unstemmed | Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy |
| title_short | Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy |
| title_sort | pro active risk analysis of an in house developed deep learning based autoplanning tool for breast volumetric modulated arc therapy |
| topic | Risk analysis FMEA In-house tool Automated breast planning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405631624001477 |
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