{"id":"W2253150690","doi":"10.1038/srep11044","title":"Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":437,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; Ontario Institute for Cancer Research","funders":"National Institutes of Health; National Cancer Institute; KWF Kankerbestrijding; Health Foundation Limburg","keywords":"Lung cancer; Medicine; Radiomics; Head and neck cancer; Stage (stratigraphy); Internal medicine; Lung; Oncology; Cancer; Radiology; Biology","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":[0.001323494,0.0002875732,0.0004706321,0.001548069,0.0002703121,0.0007074997,0.0003133077,0.000386186,0.0013517],"category_scores_gemma":[0.004744265,0.0001406232,0.0005028929,0.0009195274,0.0004029574,0.0003637482,0.0006198992,0.0003390546,0.0003087728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003430008,"about_ca_system_score_gemma":0.0002897943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001643129,"about_ca_topic_score_gemma":0.002018761,"domain_scores_codex":[0.9994591,0.0001496093,0.00005269823,0.0001469733,0.00009518749,0.00009648981],"domain_scores_gemma":[0.9976429,0.0007441324,0.0008170031,0.0003780028,0.0002812439,0.0001365862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008587592,0.00007974925,0.9420879,0.00006263806,0.0003136629,0.0001297393,0.000145234,0.004793132,0.01341283,0.0002821037,0.0006665027,0.0371677],"study_design_scores_gemma":[0.0000212911,0.0001896868,0.9793797,0.00001195236,0.00014662,0.000435461,0.0001375387,0.01497808,0.003084337,0.0008505775,0.0007384327,0.00002639294],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931412,0.0003365243,0.005249932,0.00006632422,0.000009621002,0.00002368185,0.0005365773,0.0000737216,0.0005624553],"genre_scores_gemma":[0.9984049,0.00002532844,0.001071535,0.000007431506,0.000007076344,0.0000106995,0.0003715702,0.00000636394,0.00009505345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001643129,"threshold_uncertainty_score":0.006999373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02241397381995801,"score_gpt":0.320448033344801,"score_spread":0.298034059524843,"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."}}