{"id":"W3188212740","doi":"10.3389/fgene.2021.661109","title":"Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images","year":2021,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"AI in cancer detection","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; University of Toronto; Government of Jiangsu Province","keywords":"Breast cancer; Mutation; Cancer; BRCA mutation; Magnification; Convolutional neural network; Deep learning; Oncology; Medicine; Artificial intelligence; Gene; Computer science; Internal medicine; Biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001452788,0.00007358879,0.0001229524,0.0001925067,0.00002826047,0.0000145778,0.00009615029,0.00007283774,0.000004314648],"category_scores_gemma":[0.00001975444,0.00008923583,0.0000156558,0.0003379468,0.00004819189,0.0000701532,0.00004247628,0.0001416589,2.420792e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002295031,"about_ca_system_score_gemma":0.0000821248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002741682,"about_ca_topic_score_gemma":0.00004190443,"domain_scores_codex":[0.9991517,0.0001279394,0.0001842281,0.0002755981,0.0001330987,0.0001274152],"domain_scores_gemma":[0.9996582,0.00002191562,0.0000817051,0.0001495222,0.00006506223,0.00002359027],"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":[0.00002992262,0.00004629004,0.5746667,0.00003766559,0.000003483141,0.00004482266,0.0006247545,0.1448771,0.01225267,0.00001231017,0.0001779492,0.2672263],"study_design_scores_gemma":[0.0004576381,0.00007399498,0.4098251,0.00003010739,0.000004808721,0.00002247422,0.00006171475,0.5710141,0.01795527,0.0003007348,0.0001850188,0.00006908253],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1869973,0.002578957,0.8086631,0.0003596825,0.001177402,0.00008391013,0.000009038322,0.00002451135,0.0001060808],"genre_scores_gemma":[0.8771866,0.0008219386,0.1218211,0.00007070525,0.0000392749,0.00001924303,0.000004509208,0.000008377417,0.00002827624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6901894,"threshold_uncertainty_score":0.3638932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006865642418595037,"score_gpt":0.2180995230761541,"score_spread":0.2112338806575591,"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."}}