{"id":"W4220943451","doi":"10.1002/ima.22724","title":"Machine learning based on automated breast volume scanner (<scp>ABVS</scp>) radiomics for differential diagnosis of benign and malignant <scp>BI‐RADS</scp> 4 lesions","year":2022,"lang":"en","type":"article","venue":"International Journal of Imaging Systems and Technology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Sun Yat-sen University","keywords":"Medicine; BI-RADS; Radiology; McNemar's test; Biopsy; Breast MRI; Differential diagnosis; Predictive value; Breast cancer; Breast biopsy; Prospective cohort study; Cancer; Pathology; Internal medicine; Mammography","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":[0.0007305036,0.0002171257,0.0005981441,0.001204431,0.0002258025,0.00008215866,0.0003119301,0.00009128296,0.000008897564],"category_scores_gemma":[0.001267923,0.000184618,0.0001426081,0.0002218511,0.0001950079,0.00008215355,0.0001618367,0.0008299095,4.518932e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001492862,"about_ca_system_score_gemma":0.0001248547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001398796,"about_ca_topic_score_gemma":8.489677e-7,"domain_scores_codex":[0.9979981,0.0001159949,0.0007412984,0.0002900821,0.0005665376,0.0002880043],"domain_scores_gemma":[0.9979623,0.0005142686,0.0007868412,0.0001503837,0.0004368601,0.0001493751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001628478,0.0008350544,0.9095149,0.0003755794,0.001112245,0.0008290278,0.0005022418,0.009646601,0.02168416,0.004667945,0.01227386,0.03839555],"study_design_scores_gemma":[0.004766267,0.0007257177,0.01149867,0.0008429885,0.0002300956,0.008149312,0.0009803644,0.9393014,0.0006242966,0.0002193601,0.03258545,0.00007609566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734539,0.003121583,0.01329667,0.007992532,0.001430264,0.0003404028,0.0001360821,0.0001456754,0.00008291876],"genre_scores_gemma":[0.998026,0.0002071446,0.0009602505,0.0001814098,0.0002345106,0.0000407322,0.00003953451,0.00004628576,0.0002641008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9296548,"threshold_uncertainty_score":0.7528504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006079978347424159,"score_gpt":0.2541771091957234,"score_spread":0.2480971308482993,"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."}}