{"id":"W4321435953","doi":"10.1007/s00259-023-06136-0","title":"The role of [18F]-DCFPyL PET/MRI radiomics for pathological grade group prediction in prostate cancer","year":2023,"lang":"en","type":"letter","venue":"European Journal of Nuclear Medicine and Molecular Imaging","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; Women's College Hospital; University of Toronto; University Health Network","funders":"","keywords":"Medicine; Prostate cancer; Radiomics; Magnetic resonance imaging; Histopathology; Nuclear medicine; Radiology; Pathological; Multiparametric MRI; Cancer; Pathology; 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":[0.002830988,0.0005097691,0.001097943,0.0006304206,0.001093071,0.001860438,0.001232319,0.02250374,0.002067206],"category_scores_gemma":[0.01501809,0.0004823322,0.0008349359,0.0005011067,0.002022754,0.001785429,0.0005459748,0.01422623,0.002575841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003648116,"about_ca_system_score_gemma":0.001802654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004750914,"about_ca_topic_score_gemma":0.006465698,"domain_scores_codex":[0.9984608,0.0006724685,0.000226534,0.0001479764,0.0003242039,0.0001681096],"domain_scores_gemma":[0.992534,0.005090429,0.0003251774,0.0002467594,0.001055365,0.0007481561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001444539,0.0002331558,0.01578327,0.0003729165,0.0001184514,0.03708316,0.0003948863,0.0009975927,0.003282142,0.008092015,0.7542729,0.177925],"study_design_scores_gemma":[0.0005974526,0.0007110572,0.01077598,0.000918543,0.0001837633,0.04068199,0.0004671241,0.005378717,0.002755153,0.02397996,0.9134063,0.0001440065],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.003681278,0.007681611,0.0005857439,0.9726386,0.009524557,0.00002256812,0.00008495113,0.00004679498,0.00573395],"genre_scores_gemma":[0.0863765,0.009514,0.002702742,0.8025174,0.08881731,0.0001106018,0.0001542613,0.00008961619,0.009717486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02250374,"threshold_uncertainty_score":0.02646905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525227709492927,"score_gpt":0.261495914921948,"score_spread":0.2462436378270187,"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."}}