{"id":"W4399668294","doi":"10.3390/radiation4020015","title":"Deep Texture Analysis Enhanced MRI Radiomics for Predicting Head and Neck Cancer Treatment Outcomes with Machine Learning Classifiers","year":2024,"lang":"en","type":"article","venue":"Radiation","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University; Sunnybrook Health Science Centre","funders":"","keywords":"Artificial intelligence; Radiomics; Support vector machine; Computer science; Classifier (UML); Pattern recognition (psychology); Head and neck cancer; Boosting (machine learning); Feature selection; Feature (linguistics); Magnetic resonance imaging; Machine learning; Medicine; Radiology; Radiation therapy","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":[0.001510814,0.0007016847,0.0006314989,0.001013938,0.0001771793,0.0007655282,0.000688783,0.0005745079,0.0006348445],"category_scores_gemma":[0.003477203,0.0002676606,0.0008112746,0.0005726713,0.0002185965,0.0006172248,0.0006083692,0.0008409619,0.0002826953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008401633,"about_ca_system_score_gemma":0.0006758728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005549233,"about_ca_topic_score_gemma":0.005425788,"domain_scores_codex":[0.9996481,0.0001206955,0.00002441826,0.00007874436,0.00007489415,0.00005317157],"domain_scores_gemma":[0.9990012,0.000523011,0.0001511081,0.00009631425,0.000181837,0.00004651252],"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":[0.0006091405,0.0004991202,0.05561982,0.0001344434,0.0003349896,0.0001724809,0.0001151123,0.5165875,0.01452617,0.001014733,0.002303335,0.4080831],"study_design_scores_gemma":[0.00000693292,0.00008173663,0.003263184,0.000009452305,0.0000205661,0.00002274299,0.0000108212,0.9941958,0.001374109,0.0008356922,0.0001709012,0.000008009194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6666009,0.002027904,0.3265156,0.0007483146,0.00009863303,0.0001456372,0.0006211753,0.00111106,0.002130879],"genre_scores_gemma":[0.956547,0.0001935891,0.0421495,0.00008495185,0.00003518432,0.00006745767,0.0003221736,0.0000211527,0.0005788709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005549233,"threshold_uncertainty_score":0.01103383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009199259163806069,"score_gpt":0.3044280582926168,"score_spread":0.2952287991288107,"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."}}