%0 Journal Article
%T Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.
%A Wahid KA
%A Sahin O
%A Kundu S
%A Lin D
%A Alanis A
%A Tehami S
%A Kamel S
%A Duke S
%A Sherer MV
%A Rasmussen M
%A Korreman S
%A Fuentes D
%A Cislo M
%A Nelms BE
%A Christodouleas JP
%A Murphy JD
%A Mohamed ASR
%A He R
%A Naser MA
%A Gillespie EF
%A Fuller CD
%J JCO Clin Cancer Inform
%V 8
%N 0
%D 2024 Jun
%M 38870441
暂无%R 10.1200/CCI.23.00174
%X <B>OBJECTIVE: </B>The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors.<BR><B>METHODS: </B>Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure.<BR><B>RESULTS: </B>Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations.<BR><B>CONCLUSIONS: </B>Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.