{"id":"W4412110787","doi":"10.1148/radiol.242278","title":"Impact of LI-RADS CT and MRI Ancillary Features on Diagnostic Performance: An Individual Participant Data Meta-Analysis","year":2025,"lang":"en","type":"review","venue":"Radiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen Elizabeth II Health Sciences Centre; Centre Hospitalier de l’Université de Montréal; Queen's University; McMaster University; Juravinski Hospital; Juravinski Cancer Centre; Hamilton Health Sciences; University of Toronto; Jewish General Hospital; University of Ottawa; Ottawa Hospital","funders":"Ministry of Health and Welfare; National Natural Science Foundation of China","keywords":"Medicine; Meta-analysis; Radiology; Medical physics; Nuclear medicine; Pathology","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":[0.01513833,0.002366999,0.008954963,0.001533665,0.0004354317,0.002431737,0.001351401,0.001914004,0.002818522],"category_scores_gemma":[0.03960324,0.0008233673,0.03534671,0.002536468,0.0005085679,0.001358095,0.000895814,0.00315574,0.0004104847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008918514,"about_ca_system_score_gemma":0.001456003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004541703,"about_ca_topic_score_gemma":0.008394445,"domain_scores_codex":[0.9921505,0.004807562,0.001145285,0.001119486,0.0005714873,0.0002056605],"domain_scores_gemma":[0.9793521,0.01727185,0.001299091,0.0009868867,0.0009264283,0.0001637155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.009507833,0.0000253037,0.01063611,0.05772305,0.8984246,0.0001108675,0.00004714216,0.0008112263,0.0005063537,0.0002198984,0.001504414,0.02048321],"study_design_scores_gemma":[0.001014487,0.0002921341,0.004620534,0.00220809,0.988815,0.00007502396,0.000016588,0.0002392235,0.0001843307,0.0004001381,0.002115183,0.0000193028],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01684907,0.9751177,0.003656565,0.0006220751,0.0007209095,0.0001885973,0.001918054,0.0000986737,0.0008284476],"genre_scores_gemma":[0.6273732,0.3534618,0.007779035,0.003523625,0.001226936,0.0008836774,0.004028032,0.0002241141,0.001499732],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01513833,"threshold_uncertainty_score":0.08006006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1734139689199475,"score_gpt":0.4517579780726283,"score_spread":0.2783440091526809,"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."}}