{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001955669,0.0005340656,0.0004826772,0.0008026555,0.0001426693,0.0005338778,0.0006423934,0.0005989927,0.0005233313],"category_scores_gemma":[0.004642521,0.0001875042,0.0002451424,0.0004677775,0.0003918129,0.0005724871,0.0006251762,0.0008461177,0.0001538466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007943019,"about_ca_system_score_gemma":0.00071995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006265361,"about_ca_topic_score_gemma":0.005829937,"domain_scores_codex":[0.999592,0.0001937708,0.00002054548,0.0000655207,0.00006554908,0.00006270349],"domain_scores_gemma":[0.9988812,0.0006828333,0.000164606,0.00006193551,0.0001398947,0.00006963225],"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":[0.0004314179,0.0004081787,0.07734863,0.0001475002,0.0002057278,0.0002026637,0.00009327468,0.5427217,0.002177767,0.005030761,0.005903688,0.3653286],"study_design_scores_gemma":[0.000008691949,0.00003756976,0.003352723,0.00001336928,0.00001144677,0.0000205991,0.00001173307,0.9910387,0.0007483403,0.004432533,0.0003185503,0.000005824023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6863697,0.007088559,0.296377,0.004144617,0.0001531047,0.00009597974,0.0009863424,0.001047239,0.003737571],"genre_scores_gemma":[0.9861643,0.000447757,0.01201732,0.0001510071,0.00005460683,0.00002712709,0.0003218395,0.00001072531,0.0008052718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006265361,"threshold_uncertainty_score":0.01245779,"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."}}