{"id":"W3209826882","doi":"10.1148/ryai.2021210027","title":"Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists","year":2021,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"","keywords":"Medicine; Receiver operating characteristic; Radiology; Lung cancer; Lung cancer screening; Test set; Cancer; Nuclear medicine; Retrospective cohort study; Computed tomography; Artificial intelligence; Surgery; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02180774,0.001676191,0.001433373,0.001551571,0.0007619507,0.001662746,0.001457778,0.001898035,0.001266591],"category_scores_gemma":[0.04357184,0.000479448,0.002213734,0.0007888279,0.001665543,0.001486954,0.002473464,0.001648364,0.001108789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001521726,"about_ca_system_score_gemma":0.0007546062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005500383,"about_ca_topic_score_gemma":0.007354876,"domain_scores_codex":[0.985105,0.007332972,0.0008747606,0.002855051,0.003113367,0.0007189004],"domain_scores_gemma":[0.9459079,0.02765696,0.005642889,0.007665536,0.00994585,0.003180831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01504357,0.007093638,0.7007068,0.001546395,0.005968775,0.001566433,0.003349006,0.03803442,0.0163927,0.0007395367,0.03603679,0.1735219],"study_design_scores_gemma":[0.002266115,0.01891943,0.7617485,0.0003216781,0.00242326,0.005434964,0.003314776,0.1626556,0.02364244,0.001892186,0.01674713,0.0006340066],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892606,0.001282179,0.005174044,0.0003533687,0.0001591868,0.0002271395,0.00154286,0.0004023283,0.001598315],"genre_scores_gemma":[0.9876989,0.0003180635,0.00388933,0.0002145594,0.0001368702,0.0001008376,0.006162018,0.0001758931,0.001303486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02180774,"threshold_uncertainty_score":0.1153316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05653896743861862,"score_gpt":0.3564196165415092,"score_spread":0.2998806491028906,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}