{"id":"W3185777129","doi":"10.3390/cancers13153723","title":"Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models","year":2021,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre; Princess Margaret Cancer Centre; Jewish General Hospital; University of Toronto; University Health Network","funders":"National Center for Advancing Translational Sciences","keywords":"Head and neck squamous-cell carcinoma; Medicine; Larynx; Head and neck; Basal cell; Head and neck cancer; Radiology; Pathology; Radiation therapy; Surgery","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":[0.007401006,0.0004571952,0.0006826141,0.0009837694,0.0002715603,0.001108962,0.0005740969,0.0005711595,0.0005799187],"category_scores_gemma":[0.02125939,0.0002059622,0.000920739,0.0008954919,0.0005789497,0.0005572511,0.0004777719,0.0006933679,0.0002036999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004246392,"about_ca_system_score_gemma":0.0003151667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002612492,"about_ca_topic_score_gemma":0.002463135,"domain_scores_codex":[0.9970205,0.001624153,0.0001835446,0.0007532227,0.0002899496,0.0001285968],"domain_scores_gemma":[0.9813423,0.01507364,0.001236331,0.001495156,0.0006701339,0.0001825885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009752688,0.0001884222,0.7059551,0.0001362562,0.00101635,0.0004241179,0.0002370527,0.1959663,0.009206736,0.0007091288,0.001047656,0.08413752],"study_design_scores_gemma":[0.00002657302,0.0003154043,0.5629969,0.00004407426,0.0002621881,0.0006371713,0.0001681677,0.4267489,0.003978788,0.003604247,0.001138272,0.00007926848],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688326,0.00157748,0.02790939,0.0001899351,0.00004432804,0.000033703,0.000576379,0.0001355539,0.0007005687],"genre_scores_gemma":[0.9970101,0.00009283233,0.002193598,0.00002325525,0.00001495494,0.00001281402,0.0005260895,0.00002045898,0.0001058344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007401006,"threshold_uncertainty_score":0.0391407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01450488404224773,"score_gpt":0.2753893608817958,"score_spread":0.2608844768395481,"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."}}