{"id":"W2887494772","doi":"10.3390/cancers10080249","title":"Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer","year":2018,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; École de Technologie Supérieure; McGill University Health Centre","funders":"","keywords":"Prostate cancer; Rank correlation; Random forest; Spearman's rank correlation coefficient; Artificial intelligence; Medicine; Correlation; Pattern recognition (psychology); Cancer; Nuclear medicine; Computer science; Statistics; Mathematics; 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":[0.0009075985,0.0004520161,0.0003261123,0.00158594,0.00013303,0.0005375168,0.0002616327,0.0002895057,0.0008504923],"category_scores_gemma":[0.002375901,0.00009180906,0.0003811638,0.0006579339,0.0001901992,0.0002954015,0.0002982592,0.00032492,0.000384052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002828352,"about_ca_system_score_gemma":0.0003058445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00135678,"about_ca_topic_score_gemma":0.001992826,"domain_scores_codex":[0.9997799,0.00004915419,0.00001824576,0.00005543277,0.00006860548,0.00002861935],"domain_scores_gemma":[0.9992111,0.0003329582,0.0002059745,0.00005873576,0.000146628,0.00004457192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008911958,0.0003285719,0.5846425,0.0002046588,0.0003487331,0.0001892535,0.0001074058,0.05978369,0.02996453,0.0005392503,0.002592342,0.3204077],"study_design_scores_gemma":[0.00003664164,0.0008879896,0.4066164,0.0000796087,0.0003897157,0.001231794,0.0001134707,0.5654495,0.01975596,0.002077614,0.003293384,0.00006794256],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8960518,0.00144276,0.09836877,0.0002309559,0.00003590113,0.00008680185,0.002059069,0.0004041929,0.00131971],"genre_scores_gemma":[0.9873238,0.00015918,0.01129372,0.00001647823,0.00002491046,0.00003128151,0.0009253432,0.0000111289,0.0002141017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00158594,"threshold_uncertainty_score":0.004799902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01308122373095692,"score_gpt":0.3140440009659139,"score_spread":0.300962777234957,"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."}}