{"id":"W2904291170","doi":"10.3389/fonc.2018.00630","title":"Predicting Gleason Score of Prostate Cancer Patients Using Radiomic Analysis","year":2018,"lang":"en","type":"article","venue":"Frontiers in Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; École de Technologie Supérieure","funders":"","keywords":"Prostate cancer; Spearman's rank correlation coefficient; Receiver operating characteristic; Medicine; Effective diffusion coefficient; Correlation; Magnetic resonance imaging; Rank correlation; Prostate; Pearson product-moment correlation coefficient; Imaging biomarker; Diffusion MRI; Nuclear medicine; Radiology; Cancer; Mathematics; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003755215,0.000118264,0.0006258994,0.0005464689,0.00005505326,0.000005192987,0.0001078946,0.0001237374,0.00005973809],"category_scores_gemma":[0.0003557926,0.0001066889,0.000104252,0.0008029966,0.0003406545,0.00005294799,0.00004828504,0.0003159637,9.47118e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004795804,"about_ca_system_score_gemma":0.0002445748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007649289,"about_ca_topic_score_gemma":0.00005660651,"domain_scores_codex":[0.9987085,0.0001013181,0.0004088105,0.0002756419,0.000196893,0.0003088115],"domain_scores_gemma":[0.9992867,0.00004124086,0.000250238,0.0001709748,0.0001382731,0.0001125692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002521571,0.0001453703,0.9572565,0.00004722875,0.0003669879,0.00001463264,0.001027977,0.0004001,0.0008455628,0.000003592778,0.001473245,0.03816664],"study_design_scores_gemma":[0.003467635,0.0007563987,0.5397444,0.0002169984,0.001053374,0.000008162876,0.0002748008,0.4500779,0.0004491931,0.00006930427,0.003738573,0.0001431988],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922938,0.0005777965,0.00450548,0.0003129441,0.001335521,0.0002852526,0.000007681754,0.00002102895,0.0006605429],"genre_scores_gemma":[0.9723117,0.0001371661,0.02683693,0.000306232,0.0002593101,0.00001109984,0.0000215063,0.00002009415,0.00009600499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4496778,"threshold_uncertainty_score":0.4350648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01202928362867794,"score_gpt":0.3241890691483851,"score_spread":0.3121597855197072,"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."}}