Automated contouring and statistical process control for plan quality in a breast clinical trial
Background and purpose: Automatic review of breast plan quality for clinical trials is time-consuming and has some unique challenges due to the lack of target contours for some planning techniques. We propose using an auto-contouring model and statistical process control to independently assess plan...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Physics and Imaging in Radiation Oncology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405631623000775 |
| _version_ | 1827590162322817024 |
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| author | Hana Baroudi Callistus I. Huy Minh Nguyen Sean Maroongroge Benjamin D. Smith Joshua S. Niedzielski Simona F. Shaitelman Adam Melancon Sanjay Shete Thomas J. Whitaker Melissa P. Mitchell Isidora Yvonne Arzu Jack Duryea Soleil Hernandez Daniel El Basha Raymond Mumme Tucker Netherton Karen Hoffman Laurence Court |
| author_facet | Hana Baroudi Callistus I. Huy Minh Nguyen Sean Maroongroge Benjamin D. Smith Joshua S. Niedzielski Simona F. Shaitelman Adam Melancon Sanjay Shete Thomas J. Whitaker Melissa P. Mitchell Isidora Yvonne Arzu Jack Duryea Soleil Hernandez Daniel El Basha Raymond Mumme Tucker Netherton Karen Hoffman Laurence Court |
| author_sort | Hana Baroudi |
| collection | DOAJ |
| description | Background and purpose: Automatic review of breast plan quality for clinical trials is time-consuming and has some unique challenges due to the lack of target contours for some planning techniques. We propose using an auto-contouring model and statistical process control to independently assess planning consistency in retrospective data from a breast radiotherapy clinical trial. Materials and methods: A deep learning auto-contouring model was created and tested quantitatively and qualitatively on 104 post-lumpectomy patients’ computed tomography images (nnUNet; train/test: 80/20). The auto-contouring model was then applied to 127 patients enrolled in a clinical trial. Statistical process control was used to assess the consistency of the mean dose to auto-contours between plans and treatment modalities by setting control limits within three standard deviations of the data’s mean. Two physicians reviewed plans outside the limits for possible planning inconsistencies. Results: Mean Dice similarity coefficients comparing manual and auto-contours was above 0.7 for breast clinical target volume, supraclavicular and internal mammary nodes. Two radiation oncologists scored 95% of contours as clinically acceptable. The mean dose in the clinical trial plans was more variable for lymph node auto-contours than for breast, with a narrower distribution for volumetric modulated arc therapy than for 3D conformal treatment, requiring distinct control limits. Five plans (5%) were flagged and reviewed by physicians: one required editing, two had clinically acceptable variations in planning, and two had poor auto-contouring. Conclusions: An automated contouring model in a statistical process control framework was appropriate for assessing planning consistency in a breast radiotherapy clinical trial. |
| first_indexed | 2024-03-09T01:12:00Z |
| format | Article |
| id | doaj.art-b6b4823cd8cf4f7bada8d3d1c455fbbc |
| institution | Directory Open Access Journal |
| issn | 2405-6316 |
| language | English |
| last_indexed | 2024-03-09T01:12:00Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj.art-b6b4823cd8cf4f7bada8d3d1c455fbbc2023-12-11T04:16:26ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162023-10-0128100486Automated contouring and statistical process control for plan quality in a breast clinical trialHana Baroudi0Callistus I. Huy Minh Nguyen1Sean Maroongroge2Benjamin D. Smith3Joshua S. Niedzielski4Simona F. Shaitelman5Adam Melancon6Sanjay Shete7Thomas J. Whitaker8Melissa P. Mitchell9Isidora Yvonne Arzu10Jack Duryea11Soleil Hernandez12Daniel El Basha13Raymond Mumme14Tucker Netherton15Karen Hoffman16Laurence Court17The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA; Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Corresponding author at: The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, 1400 Pressler Street, FCT8.6014, Houston, TX 77030, USA.Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Breast Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Breast Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USAThe University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USADepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Breast Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Breast Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USAThe University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA; Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USAThe University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA; Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Breast Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USAThe University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA; Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USABackground and purpose: Automatic review of breast plan quality for clinical trials is time-consuming and has some unique challenges due to the lack of target contours for some planning techniques. We propose using an auto-contouring model and statistical process control to independently assess planning consistency in retrospective data from a breast radiotherapy clinical trial. Materials and methods: A deep learning auto-contouring model was created and tested quantitatively and qualitatively on 104 post-lumpectomy patients’ computed tomography images (nnUNet; train/test: 80/20). The auto-contouring model was then applied to 127 patients enrolled in a clinical trial. Statistical process control was used to assess the consistency of the mean dose to auto-contours between plans and treatment modalities by setting control limits within three standard deviations of the data’s mean. Two physicians reviewed plans outside the limits for possible planning inconsistencies. Results: Mean Dice similarity coefficients comparing manual and auto-contours was above 0.7 for breast clinical target volume, supraclavicular and internal mammary nodes. Two radiation oncologists scored 95% of contours as clinically acceptable. The mean dose in the clinical trial plans was more variable for lymph node auto-contours than for breast, with a narrower distribution for volumetric modulated arc therapy than for 3D conformal treatment, requiring distinct control limits. Five plans (5%) were flagged and reviewed by physicians: one required editing, two had clinically acceptable variations in planning, and two had poor auto-contouring. Conclusions: An automated contouring model in a statistical process control framework was appropriate for assessing planning consistency in a breast radiotherapy clinical trial.http://www.sciencedirect.com/science/article/pii/S2405631623000775Automated segmentationRadiotherapy clinical trialBreast cancerPlan quality assurance |
| spellingShingle | Hana Baroudi Callistus I. Huy Minh Nguyen Sean Maroongroge Benjamin D. Smith Joshua S. Niedzielski Simona F. Shaitelman Adam Melancon Sanjay Shete Thomas J. Whitaker Melissa P. Mitchell Isidora Yvonne Arzu Jack Duryea Soleil Hernandez Daniel El Basha Raymond Mumme Tucker Netherton Karen Hoffman Laurence Court Automated contouring and statistical process control for plan quality in a breast clinical trial Physics and Imaging in Radiation Oncology Automated segmentation Radiotherapy clinical trial Breast cancer Plan quality assurance |
| title | Automated contouring and statistical process control for plan quality in a breast clinical trial |
| title_full | Automated contouring and statistical process control for plan quality in a breast clinical trial |
| title_fullStr | Automated contouring and statistical process control for plan quality in a breast clinical trial |
| title_full_unstemmed | Automated contouring and statistical process control for plan quality in a breast clinical trial |
| title_short | Automated contouring and statistical process control for plan quality in a breast clinical trial |
| title_sort | automated contouring and statistical process control for plan quality in a breast clinical trial |
| topic | Automated segmentation Radiotherapy clinical trial Breast cancer Plan quality assurance |
| url | http://www.sciencedirect.com/science/article/pii/S2405631623000775 |
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