{"id":"W3022777305","doi":"10.1200/cci.19.00165","title":"Quantitative Imaging Informatics for Cancer Research","year":2020,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Institutes of Health; Leidos; University of Arkansas for Medical Sciences; National Cancer Institute; University of Arkansas","keywords":"DICOM; Standardization; Health informatics; Informatics; Computer science; Health informatics tools; Data science; Implementation; Interface (matter); Medical physics; Medicine; Software engineering; Engineering; Artificial intelligence","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":[0.00276295,0.0002151758,0.0006904707,0.0001319328,0.0002394422,0.0001145934,0.0003281227,0.0001427669,0.000182327],"category_scores_gemma":[0.005937445,0.0001732371,0.00027096,0.0005158806,0.0004497913,0.0003840967,0.0001785979,0.001633271,0.00008688487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001375101,"about_ca_system_score_gemma":0.0007980912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008785977,"about_ca_topic_score_gemma":0.000008594436,"domain_scores_codex":[0.9963889,0.00008640301,0.001909353,0.0001676591,0.0008092736,0.0006384187],"domain_scores_gemma":[0.9962145,0.001550518,0.0004413796,0.0002958953,0.0008022036,0.0006954811],"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":[0.002102164,0.0002540668,0.1114057,0.00476859,0.0004918822,0.0000188312,0.02741018,0.002302773,0.0001435173,0.00654144,0.3518665,0.4926944],"study_design_scores_gemma":[0.002852924,0.0005344016,0.001644357,0.0003560073,0.0001320661,0.000005927873,0.003955563,0.5803168,0.00006913199,0.0002609387,0.4096515,0.0002203616],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5259241,0.004134227,0.2376523,0.1941586,0.0039276,0.005640979,0.0003927746,0.0007465629,0.0274229],"genre_scores_gemma":[0.48541,0.009788866,0.3156966,0.1804232,0.005854798,0.00106818,0.0002507471,0.0002412596,0.001266334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5780141,"threshold_uncertainty_score":0.7108105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2469434774881775,"score_gpt":0.5583108190181661,"score_spread":0.3113673415299886,"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."}}