Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation Article Swipe
Kareem A. Wahid
,
Onur Sahin
,
Suprateek Kundu
,
Diana Lin
,
Anthony Alanis
,
Salik Tehami
,
Serageldin Kamel
,
Simon Duke
,
Michael V. Sherer
,
Mathis Rasmussen
,
Stine Korreman
,
David Fuentes
,
Michael Cislo
,
Benjamin E. Nelms
,
John P. Christodouleas
,
James D. Murphy
,
Abdallah Mohamed
,
Renjie He
,
Mohamed A. Naser
,
Erin F. Gillespie
,
Clifton D. Fuller
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1200/cci.23.00174
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1200/cci.23.00174
PURPOSE 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. METHODS 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. RESULTS 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. CONCLUSION Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1200/cci.23.00174
- OA Status
- green
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399615520
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399615520Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1200/cci.23.00174Digital Object Identifier
- Title
-
Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian EstimationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-01Full publication date if available
- Authors
-
Kareem A. Wahid, Onur Sahin, Suprateek Kundu, Diana Lin, Anthony Alanis, Salik Tehami, Serageldin Kamel, Simon Duke, Michael V. Sherer, Mathis Rasmussen, Stine Korreman, David Fuentes, Michael Cislo, Benjamin E. Nelms, John P. Christodouleas, James D. Murphy, Abdallah Mohamed, Renjie He, Mohamed A. Naser, Erin F. Gillespie, Clifton D. FullerList of authors in order
- Landing page
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https://doi.org/10.1200/cci.23.00174Publisher landing page
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/11214868Direct OA link when available
- Concepts
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Radiation oncologist, Bayesian probability, Estimation, Similarity (geometry), Segmentation, Computer science, Bayes estimator, Artificial intelligence, Econometrics, Statistics, Data science, Medicine, Mathematics, Internal medicine, Radiation therapy, Engineering, Systems engineering, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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