{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005026962,0.0003590613,0.0008054278,0.0004770526,0.0002318775,0.0004763271,0.0001140847,0.00005128851,0.000007970149],"category_scores_gemma":[0.00001899189,0.0002672828,0.000293947,0.0002328998,0.00009841678,0.001375626,0.0000425222,0.0002300738,0.000001796354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000950573,"about_ca_system_score_gemma":0.0005480566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002138126,"about_ca_topic_score_gemma":0.000003282238,"domain_scores_codex":[0.9974809,0.00009630736,0.001007067,0.0003723577,0.0006613187,0.0003820632],"domain_scores_gemma":[0.9977668,0.0003527351,0.0009508034,0.0001721938,0.0005350912,0.0002224067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00447486,0.001023037,0.5934989,0.00249104,0.0009851112,0.0003513069,0.001656411,0.2463509,0.09276363,0.0005789305,0.001579775,0.05424614],"study_design_scores_gemma":[0.01147086,0.0003605917,0.02448388,0.004111179,0.0003845714,0.001130806,0.0004476634,0.9544018,0.0012995,0.0002432285,0.001269783,0.0003961651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6136119,0.006755779,0.3762252,0.001285312,0.00123733,0.0007777784,0.00008472925,0.00001619896,0.000005755122],"genre_scores_gemma":[0.991945,0.0003680615,0.006471142,0.0004319997,0.0004189413,0.00003000077,0.00008507258,0.00006674405,0.000183042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7080509,"threshold_uncertainty_score":0.9999779,"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."}}