{"id":"W1987054640","doi":"10.1016/j.radonc.2015.02.015","title":"CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma","year":2015,"lang":"en","type":"article","venue":"Radiotherapy and Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":706,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"National Cancer Institute; National Institutes of Health; KWF Kankerbestrijding; Stichting voor de Technische Wetenschappen; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Health Foundation Limburg","keywords":"Radiomics; Concordance; Medicine; Univariate; Imaging biomarker; Multivariate analysis; Adenocarcinoma; Multivariate statistics; Biomarker; Concordance correlation coefficient; Oncology; Radiology; Internal medicine; Magnetic resonance imaging; Cancer; Computer science; Statistics; Machine learning; Mathematics","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":[0.0002739681,0.0002763507,0.0003115622,0.001044315,0.0001538301,0.0007451227,0.0003827501,0.0005632339,0.001801417],"category_scores_gemma":[0.002128258,0.000211106,0.0003685383,0.0006426757,0.0003013024,0.0003997699,0.0003681082,0.0003735828,0.0004329386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002565562,"about_ca_system_score_gemma":0.0001613084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001469668,"about_ca_topic_score_gemma":0.001905943,"domain_scores_codex":[0.9998394,0.00004043917,0.0000249733,0.0000268234,0.00004317168,0.00002512784],"domain_scores_gemma":[0.9991595,0.0002292868,0.000304306,0.00007054282,0.0001210328,0.0001153404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008106746,0.00004872695,0.9693673,0.00004520129,0.0001076263,0.0004464024,0.00003699156,0.001135691,0.01551921,0.0001019763,0.0003043915,0.01207587],"study_design_scores_gemma":[0.00001686215,0.0001928698,0.9837047,0.00001965712,0.0001986939,0.002056674,0.0001552942,0.007693556,0.004879302,0.0003252133,0.0007379156,0.00001922131],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963323,0.000922777,0.0007693994,0.00009983342,0.00002084561,0.00001006942,0.0003063246,0.00002657561,0.001511795],"genre_scores_gemma":[0.9990651,0.0001704739,0.000280901,0.0000232849,0.00001593336,0.000003737399,0.0002188763,0.000006170049,0.0002155827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001801417,"threshold_uncertainty_score":0.006026387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0170700159655151,"score_gpt":0.3163460536078446,"score_spread":0.2992760376423295,"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."}}