{"id":"W2981358604","doi":"10.1158/1078-0432.ccr-19-1495","title":"Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death","year":2019,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Center for Advancing Translational Sciences; National Cancer Institute; Yale SPORE in Lung Cancer; Swim Across America; Yale Cancer Center; U.S. Food and Drug Administration; Melanoma Research Alliance","keywords":"Medicine; Cohort; Receiver operating characteristic; Melanoma; Population; Adjuvant therapy; Stage (stratigraphy); Oncology; Cancer; Internal medicine; Cutoff; Artificial intelligence; Computer science; Cancer research; Biology","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":[0.0004192936,0.0003152298,0.0002267755,0.0005645973,0.0001410467,0.0004844479,0.0003760235,0.000334593,0.002038009],"category_scores_gemma":[0.002049042,0.0001321716,0.0002715207,0.0002612045,0.0002218612,0.0002956875,0.0003353826,0.0003726433,0.0003745729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005240541,"about_ca_system_score_gemma":0.0004984154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003914723,"about_ca_topic_score_gemma":0.005555496,"domain_scores_codex":[0.9998728,0.00003718925,0.000008044564,0.00003740782,0.00002660123,0.0000180583],"domain_scores_gemma":[0.999639,0.000152461,0.0000644339,0.00004634065,0.000064883,0.00003288515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001636374,0.0009204312,0.3885322,0.0001407218,0.0002499259,0.0006096997,0.0001587953,0.2672713,0.03366456,0.001164936,0.004427826,0.3012233],"study_design_scores_gemma":[0.00003657816,0.0002346692,0.0840859,0.00001849256,0.00002917923,0.000250778,0.00005470522,0.9030247,0.01051897,0.001100386,0.0006325383,0.00001308101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9464946,0.0001587046,0.05006531,0.0003820971,0.00002387551,0.00008969411,0.0005844012,0.0002987484,0.001902494],"genre_scores_gemma":[0.9889219,0.00003501899,0.009934647,0.0000521307,0.000008405039,0.00002889934,0.0003306749,0.000007828616,0.0006805557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003914723,"threshold_uncertainty_score":0.00778383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08991842467784979,"score_gpt":0.4354971961962668,"score_spread":0.345578771518417,"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."}}