TY - JOUR
T1 - Automated contouring and statistical process control for plan quality in a breast clinical trial
AU - Baroudi, Hana
AU - Huy Minh Nguyen, Callistus I.
AU - Maroongroge, Sean
AU - Smith, Benjamin D.
AU - Niedzielski, Joshua S.
AU - Shaitelman, Simona F.
AU - Melancon, Adam
AU - Shete, Sanjay
AU - Whitaker, Thomas J.
AU - Mitchell, Melissa P.
AU - Yvonne Arzu, Isidora
AU - Duryea, Jack
AU - Hernandez, Soleil
AU - El Basha, Daniel
AU - Mumme, Raymond
AU - Netherton, Tucker
AU - Hoffman, Karen
AU - Court, Laurence
N1 - Funding Information:
The authors would like to acknowledge Ms. Sarah J. Bronson of the Research Medical Library at MD Anderson for her editing services. We also acknowledge the support of the High-Performance Computing for Research facility at the University of Texas MD Anderson Cancer Center for providing computational resources that have contributed to this work. This work was supported by the Tumor Measurement Initiative (TMI) through the MD Anderson Strategic Initiative Development Program (STRIDE). Dr. Hernandez is supported by a Cancer Prevention and Research Institute (CPRIT) Training Award (RP210028). This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 2043424 for Daniel El Basha. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2023 The Authors
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
KW - Automated segmentation
KW - Breast cancer
KW - Plan quality assurance
KW - Radiotherapy clinical trial
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U2 - 10.1016/j.phro.2023.100486
DO - 10.1016/j.phro.2023.100486
M3 - Article
C2 - 37712064
AN - SCOPUS:85170434997
SN - 2405-6316
VL - 28
JO - Physics and Imaging in Radiation Oncology
JF - Physics and Imaging in Radiation Oncology
M1 - 100486
ER -