{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001628753,0.0003913206,0.0003498743,0.001813929,0.0001459864,0.0004823033,0.0003022475,0.0003780325,0.0009006208],"category_scores_gemma":[0.004003112,0.0001394804,0.0005219213,0.0004646664,0.0001672635,0.0003920915,0.0003766129,0.0002780283,0.0003053874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000311987,"about_ca_system_score_gemma":0.0003582018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001381642,"about_ca_topic_score_gemma":0.002326724,"domain_scores_codex":[0.9994333,0.0002204194,0.00004970348,0.0001152447,0.0001227445,0.00005856434],"domain_scores_gemma":[0.9988605,0.0006337325,0.0001639202,0.00006847445,0.0002197594,0.00005360621],"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":[0.0011281,0.0003600321,0.629642,0.0001647955,0.0002744634,0.0003603046,0.0001163405,0.02994246,0.01855419,0.0004319563,0.002172815,0.3168526],"study_design_scores_gemma":[0.00005852276,0.000821105,0.3130538,0.00006826947,0.0002069012,0.001089893,0.0001320576,0.6682675,0.01357503,0.001103097,0.001582825,0.00004100485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604848,0.0005497212,0.03670147,0.0001652046,0.0000335831,0.0001319517,0.000468912,0.000463793,0.001000651],"genre_scores_gemma":[0.9852235,0.0001176899,0.01383564,0.00003802401,0.00001671636,0.00004772096,0.0004367414,0.0000120392,0.0002719386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001813929,"threshold_uncertainty_score":0.008613765,"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."}}