{"id":"W2767952516","doi":"10.1093/neuonc/nox168.735","title":"PATH-45. INTEGRATION OF MULTI-REGION (EPI)GENOMICS WITH MULTIMODALITY ADVANCED IMAGING HIGHLIGHTS GLIOMA INTRATUMORAL HETEROGENEITY","year":2017,"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":"Hospital for Sick Children","funders":"","keywords":"Glioma; Concordance; Radiogenomics; Genetic heterogeneity; Subtyping; Tumour heterogeneity; Biopsy; Magnetic resonance imaging; DNA methylation; Medicine; Pathology; Biology; Cancer research; Bioinformatics; Radiology; Gene; Internal medicine; Genetics; Cancer; Phenotype; Computer science","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":[0.0004225127,0.0002076977,0.0002566313,0.0004186262,0.0002271375,0.0003567024,0.0001917726,0.0002835936,0.001602671],"category_scores_gemma":[0.001112573,0.00011841,0.0003973316,0.0003784824,0.0002471021,0.0002427757,0.0004598478,0.0003410087,0.0003769858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003689674,"about_ca_system_score_gemma":0.0003178568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003003957,"about_ca_topic_score_gemma":0.005064228,"domain_scores_codex":[0.9997471,0.00005079747,0.00001310667,0.0001226084,0.00002910964,0.0000373005],"domain_scores_gemma":[0.9995963,0.0001905545,0.00007508171,0.00006090761,0.00004075886,0.00003646041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001830612,0.0001712885,0.7163656,0.0002988109,0.0005433697,0.0009462718,0.0004343748,0.0446443,0.09914862,0.001737275,0.007244974,0.1266344],"study_design_scores_gemma":[0.00007116186,0.0004457952,0.7558948,0.00002805083,0.0002456781,0.002979979,0.000260949,0.1996751,0.0244935,0.005094556,0.01076792,0.00004248377],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984706,0.0001931803,0.007412064,0.0001331364,0.00000603145,0.00001926912,0.006447114,0.0002991345,0.0007841125],"genre_scores_gemma":[0.9797284,0.00006292208,0.008232894,0.00004262616,0.000004651818,0.00002816525,0.01148553,0.00005333735,0.0003613478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003003957,"threshold_uncertainty_score":0.005972922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0239340452823193,"score_gpt":0.3315060913424892,"score_spread":0.3075720460601699,"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."}}