{"id":"W3083034492","doi":"10.2144/fsoa-2020-0073","title":"Quantitative Ultrasound Delta-Radiomics During Radiotherapy for Monitoring Treatment Responses in Head and Neck Malignancies","year":2020,"lang":"en","type":"article","venue":"Future Science OA","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Health Sciences Centre; York University; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Radiomics; Medicine; Radiation therapy; Head and neck; Ultrasound; Naive Bayes classifier; Bayes' theorem; Radiology; Lymph node; Pathology; Artificial intelligence; Surgery; Computer science; Support vector machine; Bayesian probability","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000344096,0.000150771,0.0002834821,0.0001501676,0.0002393107,0.00008188039,0.0001193158,0.00004343778,0.000005393479],"category_scores_gemma":[0.0007195463,0.0001161924,0.00005013286,0.0004566863,0.0003172594,0.0001763466,0.00001758202,0.0001589164,9.261781e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001996491,"about_ca_system_score_gemma":0.0001742553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004097782,"about_ca_topic_score_gemma":0.000005062376,"domain_scores_codex":[0.9987968,0.00002849883,0.0001917768,0.0004027528,0.0002318649,0.0003483469],"domain_scores_gemma":[0.999323,0.0001882946,0.00005736555,0.0001290449,0.00005293787,0.000249427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001464672,0.00008277573,0.2318943,0.0001280496,0.00003896902,0.00008455182,0.01490114,0.0002393046,0.7366806,0.0008787405,0.00006178726,0.01354505],"study_design_scores_gemma":[0.01029474,0.003803878,0.8790101,0.0004882571,0.00007687324,0.0003731529,0.008082592,0.03165085,0.05156581,0.0001629895,0.01390774,0.0005829574],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867033,0.002089154,0.0008108626,0.009594542,0.0003305648,0.0003763391,0.00000719131,0.00004078649,0.00004728765],"genre_scores_gemma":[0.9412962,0.001050716,0.05627923,0.0004709133,0.0007731288,0.00002882011,0.000002952095,0.00002031515,0.0000777817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6851149,"threshold_uncertainty_score":0.4738187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02720790166182937,"score_gpt":0.3436316676486708,"score_spread":0.3164237659868415,"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."}}