{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008676366,0.0006387865,0.0006069295,0.001201459,0.0001832145,0.000737216,0.0005574817,0.0005359698,0.000602533],"category_scores_gemma":[0.00210454,0.000198441,0.0008649485,0.000649792,0.0002440764,0.0005090718,0.0004505049,0.0007368368,0.0002022785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007085074,"about_ca_system_score_gemma":0.0005534669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005333612,"about_ca_topic_score_gemma":0.00448351,"domain_scores_codex":[0.9997936,0.00005474551,0.00001765329,0.00005441488,0.00004299738,0.0000366206],"domain_scores_gemma":[0.9993632,0.0003025633,0.0001066986,0.00004851222,0.0001375466,0.00004129833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008119497,0.0004739885,0.04226649,0.0001232397,0.0002516749,0.0001687535,0.00008749725,0.4041919,0.02205782,0.001109817,0.002448597,0.5260082],"study_design_scores_gemma":[0.000007732982,0.00009404858,0.003832974,0.000008362433,0.00002551479,0.00003289667,0.0000109784,0.993498,0.0015728,0.0007210703,0.000186655,0.000009019613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5423904,0.002115944,0.4508223,0.001004886,0.0001079948,0.0001710601,0.0008020226,0.0009713641,0.001614016],"genre_scores_gemma":[0.9635924,0.0002420258,0.03489532,0.00008913377,0.00005305651,0.00006758441,0.0003404543,0.00002292478,0.0006971823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005333612,"threshold_uncertainty_score":0.0106051,"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."}}