{"id":"W4322627798","doi":"10.17691/stm2023.15.1.01","title":"Radiomics in Determining Tumor-to-Normal Brain SUV Ratio Based on 11C-Methionine PET/CT in Glioblastoma","year":2023,"lang":"en","type":"article","venue":"Sovremennye tehnologii v medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ministry of Science and Higher Education of the Russian Federation","keywords":"Radiomics; Glioblastoma; Medicine; Nuclear medicine; Correlation; Magnetic resonance imaging; Radiology; Mathematics","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":[0.001924149,0.0004960972,0.0003497579,0.001033581,0.0001352031,0.0005664444,0.0002180268,0.0004813158,0.0002350919],"category_scores_gemma":[0.00305187,0.0002234852,0.0002917921,0.0003268733,0.0003261197,0.000297375,0.0002480545,0.0002277936,0.0001461417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003380336,"about_ca_system_score_gemma":0.0002012431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001505464,"about_ca_topic_score_gemma":0.0021335,"domain_scores_codex":[0.9995042,0.0002318059,0.00003764755,0.000099294,0.00008702876,0.00004001512],"domain_scores_gemma":[0.9994981,0.0002346055,0.000091763,0.0000415176,0.0000876384,0.00004634915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004962317,0.0003154444,0.7181659,0.0003405748,0.000370483,0.001596471,0.0004598477,0.01954546,0.1415907,0.0003895292,0.0005266547,0.1117366],"study_design_scores_gemma":[0.00004486963,0.001262544,0.8065583,0.00005875703,0.0005061156,0.002972359,0.0004682324,0.1363329,0.05018495,0.0007978281,0.0007392283,0.00007394466],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889135,0.001256931,0.009139827,0.00004345746,0.000009172213,0.00002743443,0.000082711,0.00005140067,0.0004755315],"genre_scores_gemma":[0.996655,0.0002451755,0.00290816,0.00000827697,0.000006454268,0.00001105358,0.00007282286,0.000007291553,0.00008579394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001924149,"threshold_uncertainty_score":0.01017594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166016329783466,"score_gpt":0.3102422034625356,"score_spread":0.293640570484189,"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."}}