{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02856677,0.001587849,0.001520975,0.01037378,0.001288758,0.008794109,0.00391984,0.002431044,0.01687695],"category_scores_gemma":[0.05946847,0.0008495694,0.002042359,0.00968052,0.003671754,0.00634445,0.006540167,0.004641489,0.01256462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005301037,"about_ca_system_score_gemma":0.00900066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002620202,"about_ca_topic_score_gemma":0.002066373,"domain_scores_codex":[0.9823681,0.007167485,0.001655938,0.002054739,0.006289712,0.000464053],"domain_scores_gemma":[0.9465798,0.02202363,0.003982705,0.01127936,0.01438332,0.001751193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001355296,0.00006681366,0.003278686,0.004220288,0.0001600292,0.0001118324,0.0006144267,0.002582136,0.003476167,0.1408813,0.2782074,0.5662654],"study_design_scores_gemma":[0.00002338773,0.00006725002,0.001530338,0.001812526,0.00009686017,0.0004684113,0.0001657821,0.00366762,0.003918987,0.06783275,0.9203332,0.00008284218],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003038954,0.06851056,0.7849346,0.05037993,0.006398486,0.001053455,0.01069358,0.02226057,0.05272985],"genre_scores_gemma":[0.04140121,0.05892175,0.832318,0.01737399,0.008575914,0.002398663,0.0160782,0.007444506,0.01548775],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02856677,"threshold_uncertainty_score":0.1510773,"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."}}