{"id":"W4388302460","doi":"10.1148/rycan.220153","title":"Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know","year":2023,"lang":"en","type":"review","venue":"Radiology Imaging Cancer","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University of Toronto; University Health Network","funders":"National Cancer Institute","keywords":"Radiogenomics; Medicine; Genomics; Precision medicine; Epigenomics; Personalized medicine; Medical physics; Bioinformatics; Pathology; Radiomics; Radiology; Genome; Genetics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006867353,0.0007873103,0.00347688,0.0008039273,0.0001353985,0.00009081922,0.0003925249,0.0002123096,0.00009155045],"category_scores_gemma":[0.0006353934,0.0006477601,0.0004450794,0.0006608543,0.0006989555,0.0001623266,0.0003225722,0.001550388,0.00003067025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001155184,"about_ca_system_score_gemma":0.001695581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001074695,"about_ca_topic_score_gemma":0.00002885282,"domain_scores_codex":[0.9957546,0.0002973736,0.001185115,0.001163595,0.0002835979,0.00131569],"domain_scores_gemma":[0.997242,0.0005459324,0.0006784947,0.0006529336,0.0001760924,0.0007045098],"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":[0.00003580509,0.00002827646,0.008337687,0.01080703,0.0004657539,0.00006445497,0.0003549081,0.00005837194,0.00003830332,0.00005804767,0.009835785,0.9699156],"study_design_scores_gemma":[0.0007807282,0.00003809032,0.0005647021,0.02849365,0.002011665,0.0003069744,0.00011573,0.001622017,0.000002324496,0.00004198184,0.9654525,0.0005696613],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004860117,0.9908928,0.0003362178,0.002748781,0.003881106,0.001277099,0.0001300954,0.0001896221,0.00005822673],"genre_scores_gemma":[0.00007527534,0.9957799,0.0007720744,0.000942357,0.001440371,0.0004158608,0.0001667664,0.0002349797,0.0001723624],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9693459,"threshold_uncertainty_score":0.9995974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03263706323466366,"score_gpt":0.4055997921946947,"score_spread":0.3729627289600311,"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."}}