{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001164478,0.0001619323,0.0002934727,0.0001568338,0.0002121853,0.0002457449,0.0000518892,0.00009715742,0.00001501943],"category_scores_gemma":[0.0006855482,0.000125652,0.0000549814,0.0002007142,0.0003708044,0.00009834269,0.00005405897,0.0002984556,9.166113e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007268207,"about_ca_system_score_gemma":0.0001986565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003573778,"about_ca_topic_score_gemma":0.00001806342,"domain_scores_codex":[0.9983315,0.00002629732,0.0002594792,0.0006821086,0.0003740267,0.0003265947],"domain_scores_gemma":[0.9988199,0.00006875674,0.0001486672,0.0003557063,0.0001561009,0.0004508873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001546796,0.00007263793,0.144217,0.0004372303,0.00008467914,0.0004458239,0.001781797,0.0002401463,0.01428351,0.00006326271,0.8137046,0.02451469],"study_design_scores_gemma":[0.002352854,0.0001262077,0.021383,0.0006471415,0.0002358027,0.002751935,0.0001875844,0.02376208,0.0004256614,0.00173039,0.946,0.0003973735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713524,0.01530855,0.001248554,0.005718663,0.005030727,0.0009172647,0.000003536863,0.0000696197,0.0003506822],"genre_scores_gemma":[0.9749154,0.0001345502,0.0134849,0.00049823,0.0005639866,0.00007043405,0.0001219541,0.00004579623,0.01016469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1322954,"threshold_uncertainty_score":0.512394,"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."}}