{"id":"W4392241449","doi":"10.3390/radiation4010005","title":"Deep Texture Analysis—Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach","year":2024,"lang":"en","type":"article","venue":"Radiation","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Health Sciences Centre; Toronto Metropolitan University; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Terry Fox Research Institute; University of Toronto; Canadian Institutes of Health Research; Terry Fox Foundation","keywords":"Radiomics; Receiver operating characteristic; Artificial intelligence; Support vector machine; Head and neck cancer; Medicine; Computer science; Classifier (UML); Radiation treatment planning; Pattern recognition (psychology); Head and neck; Machine learning; Cancer; Radiology; Radiation therapy; Internal medicine; Surgery","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002542027,0.0001586154,0.0004215808,0.0003341341,0.0000909083,0.00004323082,0.00003195626,0.00006356701,0.00001321629],"category_scores_gemma":[0.0001580482,0.0001179997,0.0001955544,0.0003810441,0.00003112691,0.00008175948,0.000008500725,0.0002327582,4.153283e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002251396,"about_ca_system_score_gemma":0.00007099539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004303766,"about_ca_topic_score_gemma":0.00005850783,"domain_scores_codex":[0.9990442,0.0000396086,0.0002722355,0.0003025401,0.0001713422,0.0001700341],"domain_scores_gemma":[0.9995211,0.0001344247,0.00009374066,0.0001180106,0.00003468682,0.00009804982],"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":[0.000152785,0.0001751446,0.5545117,0.0009277441,0.003350827,0.00001307234,0.003247608,0.06333085,0.002994003,0.000467469,0.0002590386,0.3705698],"study_design_scores_gemma":[0.001241687,0.0002155152,0.1630545,0.00008159834,0.00170479,0.00003038553,0.00006197043,0.8291768,0.0001817209,0.00002572368,0.004147763,0.00007761201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8043796,0.02205737,0.1698542,0.001930447,0.0003671796,0.0008605422,0.00008479673,0.000179416,0.0002864263],"genre_scores_gemma":[0.992043,0.001474523,0.004635875,0.0001092269,0.0002781221,0.00007959589,0.0006414654,0.00003087041,0.0007073014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7658459,"threshold_uncertainty_score":0.4811888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111141374057875,"score_gpt":0.302917550051129,"score_spread":0.2918061363105503,"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."}}