{"id":"W4392499120","doi":"10.3390/cancers16051076","title":"The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics","year":2024,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"Medical Research Council; South African Medical Research Council; National Research Foundation","keywords":"Radiogenomics; Medicine; Precision medicine; Context (archaeology); Disease; Intensive care medicine; Breast cancer; Bioinformatics; Cancer; Pathology; Internal medicine; Radiomics; Biology; Radiology","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":[0.0002208983,0.0001012521,0.0002106515,0.00004528883,0.00008574329,0.00001907394,0.00007602629,0.00003512156,0.00006052285],"category_scores_gemma":[0.00003966219,0.00006212509,0.00003799677,0.0001243996,0.0004112144,0.00003835392,0.00002277668,0.0002271104,0.000001273093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005372714,"about_ca_system_score_gemma":0.0007022455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005773788,"about_ca_topic_score_gemma":0.00005055682,"domain_scores_codex":[0.9992969,0.00002163746,0.000160935,0.0002110051,0.00008886565,0.0002206345],"domain_scores_gemma":[0.999444,0.0002065994,0.00004830523,0.0001422005,0.00003747247,0.000121431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008066646,0.00001501797,0.4189641,0.00115783,0.001302657,0.0002713977,0.003124253,0.008673589,0.01842158,0.003625012,0.02767451,0.5159633],"study_design_scores_gemma":[0.003736167,0.000712787,0.08681682,0.002623409,0.001174012,0.005534379,0.001954341,0.3132404,0.005070418,0.0005516746,0.57779,0.0007955818],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9488488,0.04207787,0.001009029,0.006818737,0.0006943495,0.0002126213,0.00003409526,0.00003916487,0.0002652925],"genre_scores_gemma":[0.9593276,0.03894078,0.0006469557,0.0005522934,0.0002748853,0.00008427859,0.00000297381,0.00002817388,0.0001420465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5501155,"threshold_uncertainty_score":0.2533388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005569263858952556,"score_gpt":0.2659851280676395,"score_spread":0.2604158642086869,"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."}}