{"id":"W4412450116","doi":"10.1186/s12880-025-01823-w","title":"Non-invasive liver fibrosis screening on CT images using radiomics","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; Toronto General Hospital; Vector Institute; Princess Margaret Cancer Centre; Canada Research Chairs; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Medicine; Radiomics; Logistic regression; Radiology; Receiver operating characteristic; Asymptomatic; Cohort; Neuroradiology; Biopsy; Ultrasound; Nuclear medicine; Artificial intelligence; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001770735,0.0006389837,0.0006578204,0.0007903596,0.0001281538,0.0005450041,0.0004274898,0.0004796997,0.0005759592],"category_scores_gemma":[0.003589275,0.0002038155,0.0006191724,0.0002875085,0.0003425551,0.0003375653,0.0002161389,0.0004163414,0.0004083287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005357974,"about_ca_system_score_gemma":0.0004430301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001614505,"about_ca_topic_score_gemma":0.002203333,"domain_scores_codex":[0.9994851,0.0002461862,0.00002603036,0.0001091265,0.0001021793,0.00003137943],"domain_scores_gemma":[0.9992535,0.0003849807,0.0001541685,0.00006723061,0.0001196688,0.00002044842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001677828,0.000676871,0.1758323,0.0002817292,0.0004621566,0.0006547452,0.0001154766,0.3395761,0.04707593,0.0009742547,0.002047346,0.4306254],"study_design_scores_gemma":[0.00003014114,0.0006276812,0.03602541,0.00003814691,0.0001135687,0.0006877949,0.00001435248,0.9504094,0.01049898,0.0006585016,0.0008662239,0.00002978104],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6266456,0.001474853,0.3673689,0.0004749773,0.00004934543,0.0002344832,0.000488543,0.001297595,0.001965633],"genre_scores_gemma":[0.9600309,0.0002451821,0.03844621,0.00009620717,0.00002407752,0.0001008094,0.0002976507,0.0000203595,0.0007386136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001770735,"threshold_uncertainty_score":0.009364665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01644999647423392,"score_gpt":0.3134208879342456,"score_spread":0.2969708914600117,"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."}}