{"doi":"10.1200/cci.23.00174","title":"Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation","abstract":"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.","journal":"JCO Clinical Cancer Informatics","year":2024,"id":498438,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":0.0,"corpus_rank":10062,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5252,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":920684,"name":"Onur Sahin","orcid":"0000-0003-2191-5659","position":1,"is_corresponding":false},{"id":525487,"name":"Suprateek Kundu","orcid":"0000-0002-1767-4875","position":2,"is_corresponding":false},{"id":890779,"name":"Diana Lin","orcid":"0000-0002-1173-0725","position":3,"is_corresponding":false},{"id":1191128,"name":"Anthony Alanis","orcid":"0000-0002-9722-7580","position":4,"is_corresponding":false},{"id":1191465,"name":"Salik Tehami","orcid":null,"position":5,"is_corresponding":false},{"id":720638,"name":"Serageldin Kamel","orcid":"0000-0002-0046-4337","position":6,"is_corresponding":false},{"id":991465,"name":"Simon Duke","orcid":null,"position":7,"is_corresponding":false},{"id":990954,"name":"Michael V. Sherer","orcid":"0000-0002-4439-7906","position":8,"is_corresponding":false},{"id":1218118,"name":"Mathis Rasmussen","orcid":"0000-0002-7853-3531","position":9,"is_corresponding":false},{"id":1191129,"name":"Stine Korreman","orcid":"0000-0002-3523-382X","position":10,"is_corresponding":false},{"id":439294,"name":"David Fuentes","orcid":"0000-0002-2572-6962","position":11,"is_corresponding":false},{"id":990955,"name":"Michael Cislo","orcid":"0000-0002-5880-2802","position":12,"is_corresponding":false},{"id":991464,"name":"Benjamin E. Nelms","orcid":null,"position":13,"is_corresponding":false},{"id":654779,"name":"John P. Christodouleas","orcid":"0000-0001-5061-2038","position":14,"is_corresponding":false},{"id":263870,"name":"James D. Murphy","orcid":"0000-0002-0523-9091","position":15,"is_corresponding":false},{"id":351182,"name":"Abdallah Mohamed","orcid":"0000-0003-2064-7613","position":16,"is_corresponding":false},{"id":426705,"name":"Renjie He","orcid":"0000-0001-9166-6286","position":17,"is_corresponding":false},{"id":653657,"name":"Mohamed A. Naser","orcid":"0000-0003-1020-4966","position":18,"is_corresponding":false},{"id":278264,"name":"Erin F. Gillespie","orcid":"0000-0002-1386-1542","position":19,"is_corresponding":false},{"id":295555,"name":"Clifton D. Fuller","orcid":"0000-0002-5264-3994","position":20,"is_corresponding":false},{"id":519105,"name":"Kareem A. Wahid","orcid":"0000-0002-0503-0175","position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":null,"created_at":"2026-07-19T02:09:42.181447Z","pmid":"38870441","pmcid":"PMC11214868","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}