{"id":"W3135830244","doi":"10.1158/1557-3265.adi21-po-030","title":"Abstract PO-030: Radiomics for head and neck cancer prognostication: results from the RADCURE machine learning challenge","year":2021,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Radiomics; Medicine; Head and neck cancer; Medical physics; Head and neck; Retrospective cohort study; Radiological weapon; Radiation therapy; Machine learning; Cohort; Artificial intelligence; Cancer; Radiology; Internal medicine; Computer science; Surgery","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01456915,0.002817787,0.002272138,0.001465063,0.0007590229,0.002165996,0.002897364,0.003470103,0.003749723],"category_scores_gemma":[0.02422515,0.0004086492,0.002254814,0.001053948,0.001151006,0.001625872,0.002915897,0.002895334,0.004814889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001392797,"about_ca_system_score_gemma":0.002673704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01137554,"about_ca_topic_score_gemma":0.008852487,"domain_scores_codex":[0.9934425,0.003411267,0.0003098182,0.001124957,0.001285992,0.0004254435],"domain_scores_gemma":[0.9870778,0.006643917,0.0004127966,0.001639597,0.002520564,0.001705229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.004481319,0.003612691,0.04926904,0.002288732,0.001759336,0.0006632055,0.0002223737,0.06789489,0.00421833,0.00186544,0.5589464,0.3047782],"study_design_scores_gemma":[0.003272463,0.007516338,0.09330776,0.001065781,0.001295282,0.002121015,0.001043409,0.696283,0.02017683,0.01372088,0.1596952,0.0005020422],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7013817,0.04801935,0.05055487,0.04739101,0.008464115,0.00212885,0.08968338,0.02332229,0.02905441],"genre_scores_gemma":[0.6883649,0.005121956,0.05888446,0.007447592,0.002345821,0.0009943205,0.2183471,0.001403376,0.01709036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01456915,"threshold_uncertainty_score":0.07704997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2039818924955499,"score_gpt":0.515686907057502,"score_spread":0.311705014561952,"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."}}