{"id":"W3007657137","doi":"10.1117/12.2560146","title":"Non-invasive prediction of lymph node risk in oral cavity cancer patients using a combination of supervised and unsupervised machine learning algorithms","year":2020,"lang":"en","type":"article","venue":"","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","funders":"","keywords":"Machine learning; Radiomics; Artificial intelligence; Medicine; Lymph node; Occult; Algorithm; Dimensionality reduction; Cancer imaging; Cancer; Radiology; Computer science; Internal medicine; Pathology","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":[0.00132763,0.0006414888,0.0008529632,0.001132757,0.0001379605,0.0005566857,0.0004415578,0.0004019009,0.0004624177],"category_scores_gemma":[0.003456978,0.0001689269,0.0005092002,0.0004786099,0.0001850055,0.000386265,0.0004366831,0.0002976212,0.0001926414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000205622,"about_ca_system_score_gemma":0.0003838957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008798242,"about_ca_topic_score_gemma":0.001509615,"domain_scores_codex":[0.9993384,0.0003461922,0.00006652059,0.00009271209,0.0001154164,0.0000406612],"domain_scores_gemma":[0.9980314,0.001229187,0.0002723434,0.0001004641,0.000286006,0.00008057691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002704432,0.001047447,0.5108858,0.0003469093,0.0007810702,0.0002335705,0.0001397019,0.08950215,0.01380557,0.0003707496,0.001185199,0.3789974],"study_design_scores_gemma":[0.00007850165,0.0011223,0.1453723,0.0000456706,0.0002438227,0.0003904908,0.00009906547,0.845065,0.005806567,0.001141853,0.0005758762,0.00005845381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9348497,0.001120306,0.06211022,0.0001476933,0.00004561136,0.000146194,0.0003736453,0.0002800426,0.0009266607],"genre_scores_gemma":[0.9738643,0.0001912638,0.02502389,0.0000237025,0.00004668714,0.00007071936,0.0004189991,0.00001243805,0.0003480126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00132763,"threshold_uncertainty_score":0.007021308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01800394426497733,"score_gpt":0.2706560972858094,"score_spread":0.2526521530208321,"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."}}