{"id":"W2600642189","doi":"10.1038/s41598-017-10371-5","title":"Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":545,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Maisonneuve-Rosemont; Jewish General Hospital; Université de Montréal; Hôpital Notre-Dame; Hôpital Fleurimont; McGill University Health Centre","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Radiomics; Medicine; Head and neck cancer; Oncology; Head and neck; Internal medicine; Radiation therapy; Risk assessment; Risk stratification; Radiology; Surgery; Computer science","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":[0.007239745,0.0006591078,0.0008958644,0.003817242,0.0002569971,0.0009299656,0.0006441845,0.0004927905,0.0007566859],"category_scores_gemma":[0.01322652,0.0002487691,0.0007777046,0.001163045,0.0005419886,0.0007081126,0.0009008384,0.0005586261,0.0003600135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006255675,"about_ca_system_score_gemma":0.000473652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001532578,"about_ca_topic_score_gemma":0.001546937,"domain_scores_codex":[0.9983125,0.0009320565,0.0001184607,0.000227324,0.000315092,0.00009458837],"domain_scores_gemma":[0.9960753,0.002166616,0.0009758228,0.0002919706,0.0003973046,0.00009291492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001436744,0.0002762023,0.3568347,0.0003396303,0.0008906724,0.0003588222,0.0003325198,0.2032093,0.01207998,0.004649411,0.002720146,0.4168719],"study_design_scores_gemma":[0.00004930952,0.0004890321,0.1420393,0.0000866647,0.0002465932,0.0007414229,0.0001502328,0.8291693,0.008125118,0.01582025,0.003002153,0.00008064361],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4718753,0.0059233,0.5159861,0.001221554,0.00009171151,0.0002577394,0.001410252,0.001048657,0.002185324],"genre_scores_gemma":[0.9622923,0.0003508483,0.03626543,0.00006417068,0.00006109041,0.00007088976,0.0005542453,0.00003506909,0.0003057995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007239745,"threshold_uncertainty_score":0.03828788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897411251840962,"score_gpt":0.3615030147370685,"score_spread":0.3425289022186588,"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."}}