{"id":"W2990533848","doi":"10.1016/j.ultrasmedbio.2019.10.024","title":"Added Value of Quantitative Ultrasound and Machine Learning in BI-RADS 4–5 Assessment of Solid Breast Lesions","year":2019,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Cancer Research Society","keywords":"BI-RADS; Receiver operating characteristic; Ultrasound; Breast imaging; Medicine; Random forest; Radiology; Biopsy; Pattern recognition (psychology); Nuclear medicine; Artificial intelligence; Computer science; Breast cancer; Mammography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001241286,0.0003193181,0.001298342,0.0009820485,0.00004262161,0.000003730453,0.0001583663,0.0001886712,0.0002893145],"category_scores_gemma":[0.001596728,0.0002443651,0.000104604,0.0009073064,0.0009882542,0.00008797396,0.00003854356,0.0008676474,0.000004516334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006417287,"about_ca_system_score_gemma":0.0001653504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002052017,"about_ca_topic_score_gemma":0.0001921013,"domain_scores_codex":[0.9973599,0.0003927895,0.0009849278,0.0005235518,0.0002661128,0.0004727274],"domain_scores_gemma":[0.9959839,0.003008425,0.0003609478,0.0003518149,0.00015365,0.0001412819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001493447,0.0002218055,0.6354728,0.0001498905,0.00007355134,0.000002221233,0.001151153,0.00006011078,0.3599916,0.001893577,0.00002860038,0.0008053303],"study_design_scores_gemma":[0.00485387,0.003309926,0.9832327,0.001114572,0.0001215457,0.000444556,0.003062413,0.0004635465,0.0007029903,0.0009910971,0.001455393,0.0002473267],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929919,0.001968936,0.0007655181,0.0005849676,0.0002424842,0.0004782122,0.00007025831,0.00003443591,0.002863235],"genre_scores_gemma":[0.9934815,0.001808698,0.004061451,0.0002256916,0.00005591429,0.00001461792,0.0002141696,0.00003116292,0.0001068475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3592886,"threshold_uncertainty_score":0.9964921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752094545408907,"score_gpt":0.3319501325660266,"score_spread":0.3144291871119376,"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."}}