{"id":"W4200240386","doi":"10.1093/neuonc/noab196.525","title":"NIMG-25. IMPROVING THE NONINVASIVE CLASSIFICATION OF GLIOMA GENETIC SUBTYPE WITH DEEP LEARNING AND DIFFUSION-WEIGHTED IMAGING","year":2021,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Glioma; Fluid-attenuated inversion recovery; Nuclear medicine; Artificial intelligence; Radiology; Computer science; Magnetic resonance imaging; Cancer research","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":[0.003028622,0.001396551,0.0008207846,0.001109126,0.0004086615,0.001101167,0.001819033,0.001574547,0.005794808],"category_scores_gemma":[0.004865616,0.0003982901,0.001263758,0.0006577025,0.0003799083,0.0006193467,0.001375266,0.001070599,0.006260515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00137158,"about_ca_system_score_gemma":0.002754258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02202847,"about_ca_topic_score_gemma":0.02515204,"domain_scores_codex":[0.9992119,0.0003274321,0.00003788401,0.0001510324,0.0001683166,0.0001034709],"domain_scores_gemma":[0.9992641,0.0001880883,0.00004748914,0.0001279794,0.0002389174,0.0001333976],"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":[0.0020145,0.0008161042,0.02837082,0.0007261725,0.001133135,0.0003463237,0.00009799824,0.06801178,0.009743392,0.001996065,0.2818934,0.6048505],"study_design_scores_gemma":[0.001656422,0.00158203,0.02351766,0.0005015485,0.0006082935,0.0004803459,0.0001417812,0.8293389,0.02755273,0.009571145,0.1048376,0.0002114618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5367289,0.0221318,0.1518121,0.01295922,0.004676722,0.003971856,0.1221786,0.09102685,0.054514],"genre_scores_gemma":[0.6212263,0.002143958,0.1706396,0.004715458,0.0004990386,0.002142369,0.1715794,0.002563108,0.02449069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02202847,"threshold_uncertainty_score":0.04380053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008333367874437536,"score_gpt":0.2677057116391958,"score_spread":0.2593723437647583,"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."}}