{"id":"W4392100000","doi":"10.1148/rycan.230029","title":"Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma","year":2024,"lang":"en","type":"article","venue":"Radiology Imaging Cancer","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Hospital; Health Sciences Centre; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Terry Fox Foundation","keywords":"Radiomics; Medicine; Head and neck squamous-cell carcinoma; Radiation therapy; Basal cell; Head and neck; Nuclear medicine; Lymph node; Head and neck cancer; Internal medicine; Oncology; Radiology; 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":[0.0008805016,0.0002520055,0.0004631683,0.0005907842,0.00008034228,0.00006456595,0.0001103279,0.00009183204,0.0000341675],"category_scores_gemma":[0.0003908992,0.0002100575,0.00004748768,0.0004922216,0.0002361789,0.000142688,0.00004592235,0.0006632791,0.000009265907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003859385,"about_ca_system_score_gemma":0.0004763605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008261956,"about_ca_topic_score_gemma":0.00003846167,"domain_scores_codex":[0.998117,0.0002567609,0.0003352718,0.0006096668,0.0002005785,0.0004807035],"domain_scores_gemma":[0.9988107,0.000579425,0.00006430558,0.0002287244,0.00004743354,0.00026938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001577634,0.00004223012,0.9735657,0.0001731915,0.00004829441,0.000510919,0.002876283,0.00169233,0.003925667,0.0001309567,0.00433987,0.01111691],"study_design_scores_gemma":[0.004043792,0.001059398,0.8490721,0.0003819049,0.0001718835,0.001113156,0.0002186485,0.1267876,0.000362252,0.00006814919,0.01639443,0.0003267009],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9655019,0.01761043,0.00226125,0.01332554,0.0004052295,0.0005255255,0.00004176679,0.0001108319,0.0002175285],"genre_scores_gemma":[0.990779,0.0002578725,0.005532304,0.002861588,0.000211532,0.00009612134,0.00003556094,0.00006345447,0.0001626102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1250953,"threshold_uncertainty_score":0.8565897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01032922213436041,"score_gpt":0.3131832130083771,"score_spread":0.3028539908740167,"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."}}