Automated Contouring and Statistical Process Control for Plan Quality in a Breast Clinical Trial

Authors

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

Publication Date

10-1-2023

Journal

Physics and Imaging in Radiation Oncology

Abstract

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.

Keywords

Automated segmentation, Radiotherapy clinical trial, Breast cancer, Plan quality assurance

Comments

PMID: 37712064

DOI

10.1016/j.phro.2023.100486

PMID

37712064

PMCID

PMC10498301

PubMedCentral® Posted Date

August 2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes