{"id":"W2936340812","doi":"10.1007/s00330-019-06159-y","title":"Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning","year":2019,"lang":"en","type":"article","venue":"European Radiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":119,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Royal Victoria Hospital; Jewish General Hospital; Université de Montréal; Royal Victoria Regional Health Centre; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé; Réseau de cancérologie Rossy","keywords":"Medicine; Neuroradiology; Head and neck squamous-cell carcinoma; Lymph node metastasis; Cervical lymph nodes; Radiology; Interventional radiology; Head and neck; Basal cell; Lymph node; Ultrasound; Head and neck cancer; Metastasis; Radiation therapy; Pathology; Internal medicine; Surgery; Cancer; Neurology","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":[0.0002693173,0.0002107854,0.0002636316,0.001100546,0.0001196524,0.0005229454,0.0002183309,0.0003196979,0.0005751958],"category_scores_gemma":[0.0008550435,0.0001369299,0.0003092519,0.0004554294,0.0001488406,0.0002271588,0.0002413009,0.0002159925,0.0001615407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002400729,"about_ca_system_score_gemma":0.0003088826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003426452,"about_ca_topic_score_gemma":0.004870666,"domain_scores_codex":[0.9999071,0.0000187695,0.000009672654,0.00001704043,0.00003186867,0.00001561085],"domain_scores_gemma":[0.9998585,0.0000497657,0.00002529687,0.000008242068,0.00004058095,0.00001771916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002148371,0.0004984605,0.5068066,0.000228984,0.0003119234,0.0005576347,0.00006294606,0.02369834,0.08506981,0.0004122467,0.001729824,0.3784748],"study_design_scores_gemma":[0.00005413756,0.0003995557,0.3304293,0.00002889479,0.0002733171,0.001339386,0.0001805084,0.6430969,0.02192325,0.0008062577,0.001427019,0.00004122717],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855134,0.0006360849,0.0126577,0.0001806145,0.0000334525,0.00003449435,0.0002549753,0.00005034075,0.000638876],"genre_scores_gemma":[0.9943712,0.0002535876,0.004619424,0.00001795322,0.00001631451,0.00001158579,0.0001676239,0.000005091375,0.0005372461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003426452,"threshold_uncertainty_score":0.006813049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005765094514935071,"score_gpt":0.2201333359373833,"score_spread":0.2143682414224482,"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."}}