{"id":"W4400804052","doi":"10.1093/jbi/wbae037","title":"Utilization of Texture Analysis in Differentiating Benign and Malignant Breast Masses: Comparison of Grayscale Ultrasound, Shear Wave Elastography, and Radiomic Features","year":2024,"lang":"en","type":"article","venue":"Journal of Breast Imaging","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Joseph’s Healthcare Hamilton; York University; Hospital for Sick Children; McMaster University; Juravinski Hospital; SickKids Foundation; University of Toronto","funders":"","keywords":"Malignancy; Medicine; Grayscale; Radiology; Elastography; Logistic regression; Radiomics; Nuclear medicine; Ultrasound; Pathology; Internal medicine; Artificial intelligence; Computer science; Pixel","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":[0.0004542734,0.0002058683,0.0007973155,0.001782587,0.00005767979,0.00007156652,0.00007307534,0.0000642571,0.00001832484],"category_scores_gemma":[0.00004013992,0.0001620138,0.0003202398,0.001158125,0.0002552953,0.000261598,0.00002658383,0.0004217637,9.633187e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003857033,"about_ca_system_score_gemma":0.00007846791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001285907,"about_ca_topic_score_gemma":0.00002110084,"domain_scores_codex":[0.9982681,0.00008000019,0.0007713769,0.0002316507,0.0004336498,0.0002152434],"domain_scores_gemma":[0.9988886,0.0002740673,0.0003841321,0.000141179,0.0001771136,0.0001349229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001582354,0.000161588,0.9366696,0.0004910897,0.0007960178,0.00002261281,0.003156901,0.00007079464,0.04777173,0.00006064309,0.00006416129,0.01057659],"study_design_scores_gemma":[0.000963182,0.00009338264,0.974893,0.001739407,0.001921522,0.01151784,0.003169295,0.004589264,0.0007305539,0.000216185,0.00002386293,0.0001424738],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801651,0.00827412,0.01084787,0.0003923725,0.00008511081,0.0000915901,0.00004212253,0.00001469968,0.00008705945],"genre_scores_gemma":[0.9963982,0.0003254692,0.003137994,0.00001525374,0.00006859165,7.119378e-7,0.0000146065,0.00002306043,0.00001617911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04704118,"threshold_uncertainty_score":0.660673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009574988471326361,"score_gpt":0.2726080969729444,"score_spread":0.263033108501618,"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."}}