Faculty, Staff and Student Publications
Publication Date
7-1-2023
Journal
Journal of Applied Clinical Medical Physics
Abstract
PURPOSE: Variability in contouring contributes to large variations in radiation therapy planning and treatment outcomes. The development and testing of tools to automatically detect contouring errors require a source of contours that includes well-understood and realistic errors. The purpose of this work was to develop a simulation algorithm that intentionally injects errors of varying magnitudes into clinically accepted contours and produces realistic contours with different levels of variability.
METHODS: We used a dataset of CT scans from 14 prostate cancer patients with clinician-drawn contours of the regions of interest (ROI) of the prostate, bladder, and rectum. Using our newly developed Parametric Delineation Uncertainties Contouring (PDUC) model, we automatically generated alternative, realistic contours. The PDUC model consists of the contrast-based DU generator and a 3D smoothing layer. The DU generator transforms contours (deformation, contraction, and/or expansion) as a function of image contrast. The generated contours undergo 3D smoothing to obtain a realistic look. After model building, the first batch of auto-generated contours was reviewed. Editing feedback from the reviews was then used in a filtering model for the auto-selection of clinically acceptable (minor-editing) DU contours.
RESULTS: Overall, C values of 5 and 50 consistently produced high proportions of minor-editing contours across all ROI compared to the other C values (0.936
DISCUSSION: The proposed methodology and subsequent results are promising and could have a great impact on treatment planning by generating mathematically simulated alternative structures that are clinically relevant and realistic enough (i.e., similar to clinician-drawn contours) to be used in quality control of radiation therapy.
Keywords
Male, Humans, Prostatic Neoplasms, Tomography, X-Ray Computed, Prostate, Rectum, Urinary Bladder, Radiotherapy Planning, Computer-Assisted
DOI
10.1002/acm2.13970
PMID
37078392
PMCID
PMC10338799
PubMedCentral® Posted Date
4-20-2023
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes