{"id":"W993201291","doi":"10.1118/1.4925587","title":"TU‐CD‐BRB‐02: BEST IN PHYSICS (JOINT IMAGING‐THERAPY): Identification of Molecular Phenotypes by Integrating Radiomics and Genomics","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Clinical Research Institute; University Health Network","funders":"","keywords":"Radiomics; Concordance; Medicine; Phenotype; Computational biology; Oncology; Bioinformatics; Biology; Internal medicine; Gene; Genetics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001215101,0.0005017634,0.0005663526,0.001092241,0.0002799613,0.001005029,0.0004457275,0.0004524334,0.002094771],"category_scores_gemma":[0.001683278,0.0002065865,0.0003916542,0.0009086901,0.0003499011,0.0002800734,0.0006694959,0.0004770842,0.0007300419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005300983,"about_ca_system_score_gemma":0.0003986151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001784527,"about_ca_topic_score_gemma":0.002080426,"domain_scores_codex":[0.9995555,0.0001208763,0.00002331147,0.0001178841,0.0001065351,0.00007583358],"domain_scores_gemma":[0.9993345,0.0001384359,0.0002811234,0.00009006665,0.00006013985,0.00009564841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002514067,0.0004314617,0.8474405,0.0001435952,0.0003983517,0.0001930892,0.0001129032,0.00850101,0.07574388,0.0004915238,0.002107083,0.06192247],"study_design_scores_gemma":[0.0001301458,0.0008038143,0.9355127,0.00002127294,0.0003633972,0.001392876,0.00009034015,0.03338975,0.02298026,0.0009850108,0.004296425,0.00003415212],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927787,0.0004578987,0.003789128,0.0001252714,0.000006929452,0.00003256274,0.001596482,0.0002102855,0.001002621],"genre_scores_gemma":[0.9945379,0.00008114641,0.002336462,0.00005302299,0.000007895172,0.00003318461,0.00250109,0.0000452052,0.0004040173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002094771,"threshold_uncertainty_score":0.007007718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01355395608792133,"score_gpt":0.2816673503320126,"score_spread":0.2681133942440913,"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."}}