{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001708695,0.0005923883,0.0003607484,0.001538455,0.0001930712,0.0007640233,0.0005550531,0.000424363,0.0006072486],"category_scores_gemma":[0.004524575,0.0001949523,0.0004571712,0.0004900374,0.0004009326,0.0005571754,0.000679418,0.0005600341,0.000244734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009331986,"about_ca_system_score_gemma":0.0006374299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004093327,"about_ca_topic_score_gemma":0.004416441,"domain_scores_codex":[0.9994856,0.0001955494,0.00003236738,0.0001102172,0.0001289628,0.00004729603],"domain_scores_gemma":[0.9988725,0.000438048,0.000315116,0.0001004531,0.0002105429,0.00006341128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004041567,0.0003613427,0.2489227,0.0001305026,0.0002846344,0.0001989963,0.00007419919,0.4028858,0.007016325,0.00134738,0.002201333,0.3361727],"study_design_scores_gemma":[0.00000971219,0.00007548387,0.01527631,0.00002379996,0.00003024867,0.00008878758,0.00001239349,0.9798421,0.002361676,0.001900112,0.0003658412,0.00001358944],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.738547,0.001807364,0.2540166,0.00132837,0.00007516625,0.0001601928,0.0006234227,0.0008671887,0.002574532],"genre_scores_gemma":[0.9765007,0.0001782569,0.02226081,0.0001036031,0.00003147309,0.00002894083,0.000331992,0.00001341435,0.0005508827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004093327,"threshold_uncertainty_score":0.009036601,"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."}}