{"id":"W3011720109","doi":"10.1038/s41598-020-61297-4","title":"Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; Stichting voor de Technische Wetenschappen; National Institute of Dental and Craniofacial Research; Center for Translational Molecular Medicine; National Cancer Institute; KWF Kankerbestrijding; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Health Foundation Limburg; National Science Foundation; Eurostars; Horizon 2020 Framework Programme; Cancer Research UK; Andrew Sabin Family Foundation; Division of Mathematical Sciences; University of Texas MD Anderson Cancer Center; Interreg; Elekta","keywords":"Radiomics; Feature selection; Logistic regression; Computer science; Workflow; Feature (linguistics); Artificial intelligence; Cluster analysis; Machine learning; Receiver operating characteristic; Head and neck cancer; Data mining; Cancer; Medicine; Database; Internal medicine","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":[0.00312013,0.0003861374,0.0006680994,0.0003841018,0.000270267,0.0009227475,0.0009279029,0.00046541,0.0007707418],"category_scores_gemma":[0.006021595,0.00022799,0.0005197922,0.0004578051,0.0005556531,0.001037665,0.0009437721,0.000575792,0.0002940611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006531529,"about_ca_system_score_gemma":0.0007749936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001576822,"about_ca_topic_score_gemma":0.001132993,"domain_scores_codex":[0.9985901,0.0006341007,0.00006264327,0.0004067623,0.0001975749,0.0001087819],"domain_scores_gemma":[0.9965928,0.001586798,0.0003899958,0.0009217568,0.0004022593,0.0001063578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001562607,0.0007242152,0.05712377,0.00009416266,0.0002732528,0.0002685798,0.0001705999,0.649428,0.0138065,0.002187078,0.002542402,0.2718189],"study_design_scores_gemma":[0.00003247939,0.0001740684,0.005415475,0.000005677244,0.00002774871,0.00007899844,0.00003625546,0.985288,0.005151584,0.003347245,0.000432994,0.000009426331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4762053,0.0004319513,0.5199673,0.0006267771,0.00007100577,0.00008304203,0.000341927,0.001173814,0.001098987],"genre_scores_gemma":[0.9811651,0.0000470278,0.01804712,0.00004315654,0.00003028876,0.00003668626,0.0002079975,0.0000154583,0.0004071903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00312013,"threshold_uncertainty_score":0.01650101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01776262357648538,"score_gpt":0.2961112534293367,"score_spread":0.2783486298528513,"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."}}