{"id":"W3125770257","doi":"10.1038/s41598-021-81272-x","title":"Utility of T2-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: a single-arm, multicenter study","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network; Sinai Health System; Lunenfeld-Tanenbaum Research Institute; Institute of Cancer Research","funders":"Helse Midt-Norge; Norges Teknisk-Naturvitenskapelige Universitet; Kreftforeningen","keywords":"Histogram; Medicine; Pattern recognition (psychology); Prostate cancer; Effective diffusion coefficient; Artificial intelligence; Principal component analysis; Nuclear medicine; Magnetic resonance imaging; Radiology; Cancer; Computer science; Internal medicine","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":[0.008691822,0.0007479809,0.0006297091,0.0006292462,0.0004601133,0.0007946842,0.0003204064,0.0004676589,0.0005035563],"category_scores_gemma":[0.007678533,0.0002825993,0.0005562119,0.0003915303,0.0005592753,0.0007197209,0.0004944251,0.0002936941,0.0002493756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002038568,"about_ca_system_score_gemma":0.0002601413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006425388,"about_ca_topic_score_gemma":0.0006411023,"domain_scores_codex":[0.9975948,0.001287523,0.000188498,0.0005649139,0.0002610475,0.000103259],"domain_scores_gemma":[0.9959142,0.000897544,0.001135837,0.0008592765,0.000846522,0.0003466833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01020952,0.00106913,0.9429488,0.00005832409,0.0008170176,0.0002086195,0.0006080338,0.0005060586,0.02063266,0.00004625701,0.0001961871,0.02269951],"study_design_scores_gemma":[0.000268942,0.01574864,0.9741364,0.00001786499,0.0005691255,0.0008612358,0.0003637235,0.002710699,0.004502018,0.00005848606,0.0007209785,0.00004184149],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984897,0.0001884129,0.0009575444,0.00001311023,0.000006439909,0.0000680788,0.00008690562,0.00001227795,0.0001775025],"genre_scores_gemma":[0.9991909,0.0000331153,0.0005500297,0.00001338465,0.00001330694,0.00003533232,0.0001140069,0.000005379309,0.00004450598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008691822,"threshold_uncertainty_score":0.04596728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331472785565964,"score_gpt":0.3338655549989416,"score_spread":0.320550827143282,"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."}}