{"id":"W4386346473","doi":"10.1158/0008-5472.can-23-1856","title":"Using Machine Learning to Predict <i>TP53</i> Mutation Status and Aggressiveness of Prostate Cancer from Routine Histology Images","year":2023,"lang":"en","type":"letter","venue":"Cancer Research","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier de l'Université Laval","funders":"","keywords":"Prostate cancer; Cancer; Biomarker; Mutation; Histology; Medicine; Oncology; Prostate; Internal medicine; Biology; Genetics; Gene","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":[0.002737564,0.000486669,0.0006407535,0.0009211282,0.0004424694,0.001386443,0.001099618,0.005362045,0.001731178],"category_scores_gemma":[0.01668645,0.0003418218,0.0005996648,0.0004505636,0.001098739,0.001222006,0.0004307435,0.006054092,0.003937578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132318,"about_ca_system_score_gemma":0.0004346239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001477893,"about_ca_topic_score_gemma":0.002268058,"domain_scores_codex":[0.9988654,0.0004444931,0.00008632182,0.0001584809,0.0003849879,0.00006036093],"domain_scores_gemma":[0.9928531,0.004905496,0.0001933701,0.0004347883,0.001388812,0.0002243625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006418643,0.0002378001,0.01213283,0.0003930621,0.000173706,0.002086403,0.0001251505,0.002222462,0.004936629,0.006476306,0.527174,0.4433999],"study_design_scores_gemma":[0.0007712261,0.0008388403,0.01372691,0.0008086864,0.0003361171,0.01490835,0.0003638662,0.128807,0.0227617,0.1136841,0.7027193,0.0002737887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01798538,0.02738246,0.04188713,0.8669471,0.03092783,0.0001396593,0.0004018004,0.001005926,0.01332278],"genre_scores_gemma":[0.2979835,0.04833663,0.05317667,0.4637791,0.1005149,0.0005521799,0.0008879853,0.0005385143,0.03423045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005362045,"threshold_uncertainty_score":0.01447779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07832069754635498,"score_gpt":0.3912860821213869,"score_spread":0.312965384575032,"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."}}