{"id":"W2923027365","doi":"10.1038/s41598-019-39206-1","title":"Deep learning in head & neck cancer outcome prediction","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":200,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Radiomics; Convolutional neural network; Artificial intelligence; Computer science; Head and neck cancer; Head and neck squamous-cell carcinoma; Outcome (game theory); Appropriate Use Criteria; Head and neck; Basal cell; Deep learning; Medical imaging; Machine learning; Medicine; Pattern recognition (psychology); Radiology; Radiation therapy; Internal medicine; Surgery; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001550087,0.0001190322,0.0002814415,0.0002567947,0.0001150881,0.000096237,0.00006036269,0.00007315354,0.0007719782],"category_scores_gemma":[0.0005546977,0.0001012239,0.00008603614,0.0004560649,0.00009949032,0.0001227668,0.00004708046,0.000593093,0.000123715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001418063,"about_ca_system_score_gemma":0.000123947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002646379,"about_ca_topic_score_gemma":0.0000846452,"domain_scores_codex":[0.9979284,0.00004045271,0.0005440052,0.0006063731,0.0005423067,0.0003384434],"domain_scores_gemma":[0.9990757,0.00002705445,0.0001741169,0.000475883,0.00008137424,0.0001659343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001173735,0.00004408515,0.9739534,0.00006891937,0.000009917097,0.0001803378,0.0003242197,0.004134229,0.007760718,0.00001035094,0.001523412,0.01197867],"study_design_scores_gemma":[0.000841281,0.00005368742,0.5391226,0.0003486987,0.00003601928,0.0005927635,0.00008673031,0.0476508,0.0001970431,0.0003782958,0.4105077,0.0001843343],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984638,0.0003343718,0.0004047113,0.001008269,0.007976825,0.0003221056,2.426565e-7,0.0001167827,0.005198657],"genre_scores_gemma":[0.9646564,0.00001571609,0.0008934097,0.000199895,0.0001450358,0.00001773837,0.00005528873,0.00002246663,0.03399402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4348308,"threshold_uncertainty_score":0.845262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01797630220835295,"score_gpt":0.3243917913262709,"score_spread":0.3064154891179179,"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."}}