{"id":"W3111168043","doi":"10.1093/neuonc/noaa215.649","title":"NIMG-36. AUTOMATIC STRATIFICATION OF ENHANCING AND NON-ENHANCING GLIOMAS INTO GENETIC SUBTYPES USING DEEP NEURAL NETWORKS AND DIFFUSION-WEIGHTED IMAGING","year":2020,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"","keywords":"Fluid-attenuated inversion recovery; Nuclear medicine; Medicine; Wild type; Magnetic resonance imaging; Mutant; Radiology; Biology; Genetics; Gene","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":[0.000749169,0.001801656,0.0007347272,0.001207086,0.0003975463,0.0007024287,0.001471232,0.00146298,0.00804595],"category_scores_gemma":[0.001191643,0.000577295,0.001329917,0.0007923,0.000266473,0.0004306732,0.0008763557,0.0006262757,0.005420886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001210794,"about_ca_system_score_gemma":0.001491653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03963104,"about_ca_topic_score_gemma":0.03696292,"domain_scores_codex":[0.9997299,0.00005145176,0.00001435752,0.00009806288,0.00005564445,0.00005053624],"domain_scores_gemma":[0.9998099,0.00002786366,0.00001874319,0.00004110072,0.0000701461,0.00003222097],"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":[0.001401495,0.0004358305,0.007568364,0.0006009378,0.0007080055,0.0004883228,0.0001011052,0.1236084,0.01439989,0.001969403,0.2383743,0.6103439],"study_design_scores_gemma":[0.0004350058,0.0003383846,0.007080673,0.00009455409,0.0001372438,0.0002329161,0.00005193217,0.9259271,0.01821945,0.006490923,0.04089814,0.00009362278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3266591,0.007672292,0.2503817,0.002862087,0.001708611,0.003298682,0.1369433,0.2480753,0.02239888],"genre_scores_gemma":[0.4509073,0.001357144,0.3117593,0.001380848,0.0002184463,0.002328481,0.1940214,0.003572291,0.03445469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03963104,"threshold_uncertainty_score":0.07880074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008937830575716326,"score_gpt":0.2789556554477138,"score_spread":0.2700178248719974,"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."}}