{"id":"W4399875001","doi":"10.1200/cci.23.00184","title":"Prostate Cancer Risk Stratification by Digital Histopathology and Deep Learning","year":2024,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Prostate Cancer Canada","keywords":"Histopathology; Prostate cancer; Medicine; Grading (engineering); Prostatectomy; Concordance; Risk stratification; Risk assessment; Nomogram; Artificial intelligence; Radiology; Oncology; Pathology; Cancer; Computer science; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006503097,0.0001641241,0.0002249503,0.00007417588,0.0001580964,0.0006140973,0.0003207561,0.0001566417,0.00002207542],"category_scores_gemma":[0.0001703649,0.0001462714,0.00007020499,0.0003222073,0.0001655771,0.001726444,0.0001597739,0.0007149155,0.00005046835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002054665,"about_ca_system_score_gemma":0.0001975984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007736922,"about_ca_topic_score_gemma":0.00005524878,"domain_scores_codex":[0.9982156,0.00007024692,0.0008868114,0.0002937508,0.0002673932,0.0002662168],"domain_scores_gemma":[0.9988049,0.0003433265,0.0003273201,0.0002967532,0.0001003093,0.000127371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001205555,0.00001383812,0.0137614,0.0001113251,0.00001824593,0.000002022286,0.002612204,0.0006037679,0.00001116835,0.000670873,0.002915564,0.9792675],"study_design_scores_gemma":[0.0004254349,0.0003600892,0.007248058,0.0001124893,0.00005028518,0.00001925635,0.0002597668,0.640079,0.000109508,0.002617601,0.3483212,0.0003973308],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1811175,0.01710833,0.7870991,0.002512631,0.006317648,0.0007812837,0.0001636874,0.001234015,0.003665815],"genre_scores_gemma":[0.9747787,0.01647563,0.006290357,0.000574993,0.000377693,0.0002238463,0.00002162201,0.00002837533,0.001228781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9788702,"threshold_uncertainty_score":0.5964774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02225807846600339,"score_gpt":0.3411788368971125,"score_spread":0.3189207584311091,"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."}}