{"id":"W2997637596","doi":"","title":"Zone-DR: Discovery Radiomics via Zone-level Deep Radiomic Sequencer Discovery for Zone-based Prostate Cancer Grading using Diffusion Weighted Imaging","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Radiomics; Prostate cancer; Grading (engineering); Medicine; Prostate; Diffusion MRI; Medical physics; Artificial intelligence; Radiology; Cancer; Computer science; Magnetic resonance imaging; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001472116,0.0007225557,0.0007014975,0.001371866,0.0002745371,0.0009329403,0.00114462,0.0007874689,0.001076355],"category_scores_gemma":[0.003030313,0.0003335916,0.0007073764,0.0005940053,0.0005239274,0.001120558,0.001474156,0.0007998988,0.0006084188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004390851,"about_ca_system_score_gemma":0.0007278281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009292407,"about_ca_topic_score_gemma":0.001529321,"domain_scores_codex":[0.9993201,0.0001825483,0.00004420526,0.0001846251,0.0001944549,0.00007408771],"domain_scores_gemma":[0.9987871,0.0003963985,0.0002446138,0.0002467753,0.0002367794,0.00008837933],"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":[0.001083301,0.0002448758,0.01198626,0.0005438611,0.0002130056,0.000481109,0.0003240847,0.1074589,0.2519467,0.01121806,0.002799161,0.6117007],"study_design_scores_gemma":[0.00008238605,0.0007568044,0.005267079,0.00002755837,0.0001446632,0.001532281,0.0001195067,0.8416529,0.1331947,0.01144934,0.005670412,0.0001023982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05769622,0.0006928701,0.9383805,0.0001742129,0.00003263693,0.0000992545,0.0001655852,0.001632272,0.001126571],"genre_scores_gemma":[0.486174,0.0003661596,0.5106,0.0002037668,0.00004301791,0.00008647907,0.0004481013,0.0001468346,0.001931616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001472116,"threshold_uncertainty_score":0.00778538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01716609580041932,"score_gpt":0.3004415645849757,"score_spread":0.2832754687845564,"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."}}