{"id":"W2606926876","doi":"10.1038/srep46349","title":"Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":277,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Radiomics; Feature selection; Redundancy (engineering); Random forest; Lung cancer; Feature (linguistics); Artificial intelligence; Computer science; Medicine; Pattern recognition (psychology); Oncology","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":[0.001126216,0.0004813227,0.0006149398,0.00161531,0.0001662192,0.0004823028,0.0002759623,0.0002416642,0.0004847943],"category_scores_gemma":[0.003302724,0.0001245626,0.0005747405,0.0007015788,0.0002250249,0.0004341504,0.0003617388,0.0002165986,0.000211075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003787805,"about_ca_system_score_gemma":0.0003464691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002111019,"about_ca_topic_score_gemma":0.003127757,"domain_scores_codex":[0.9997432,0.00009183182,0.00002516211,0.00004413234,0.00006488456,0.00003091733],"domain_scores_gemma":[0.9990891,0.0003748894,0.0001806237,0.0001099338,0.0001988431,0.00004648819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00138943,0.0002191504,0.4591243,0.0001779254,0.0004637091,0.0005156869,0.0001503793,0.1740384,0.03446936,0.0007302812,0.00130537,0.3274162],"study_design_scores_gemma":[0.00003179911,0.000796913,0.3150926,0.00003154653,0.0004130698,0.0007637436,0.0001388844,0.6631563,0.01573031,0.002731637,0.001050486,0.00006265322],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8704717,0.001340918,0.1255457,0.0002147562,0.00002191091,0.0001109678,0.0008191267,0.0006280402,0.0008467825],"genre_scores_gemma":[0.99121,0.0001300615,0.008097457,0.00001045962,0.000009472248,0.00001961204,0.0004143365,0.000008154989,0.0001005278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002111019,"threshold_uncertainty_score":0.005956054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01683635680560899,"score_gpt":0.3190334353009178,"score_spread":0.3021970784953088,"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."}}