{"id":"W4282969792","doi":"10.1158/1538-7445.am2022-1685","title":"Abstract 1685: Deep learning approaches to deciphering intra-tumoural heterogeneity in glioblastoma","year":2022,"lang":"en","type":"article","venue":"Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"","keywords":"Workflow; Precision medicine; Tumor heterogeneity; Computational biology; Cluster analysis; Glioblastoma; Pharmacogenomics; Tumour heterogeneity; Computer science; Laser capture microdissection; Artificial intelligence; Bioinformatics; Biology; Machine learning; Cancer; Cancer research; Database","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":[0.001271828,0.0007154162,0.0005204476,0.001113586,0.0003209794,0.001046584,0.001018144,0.0008853784,0.002099282],"category_scores_gemma":[0.002028054,0.0003031843,0.0005859533,0.0009020044,0.0004890491,0.0007509585,0.001335123,0.001377958,0.0006391567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001143165,"about_ca_system_score_gemma":0.001188852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006653931,"about_ca_topic_score_gemma":0.006622106,"domain_scores_codex":[0.9996743,0.00008698424,0.00002130718,0.00008345057,0.00008747564,0.00004642846],"domain_scores_gemma":[0.9995253,0.0001686662,0.00005945253,0.00005400782,0.0001385598,0.00005396303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002915046,0.0002624781,0.01113974,0.000311379,0.0002651962,0.0001562784,0.0001406201,0.4897798,0.03052346,0.01227889,0.01570231,0.4391484],"study_design_scores_gemma":[0.000009072414,0.00003565026,0.001437594,0.00001578209,0.00001802124,0.00001833355,0.00002018014,0.9834064,0.004125313,0.009389341,0.001515234,0.000009049722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1217092,0.002589015,0.8632258,0.002625055,0.0002060041,0.0001364374,0.002004167,0.002862259,0.004642047],"genre_scores_gemma":[0.6906783,0.00145783,0.2943891,0.0008953068,0.0002062305,0.0002667558,0.004530237,0.0003531478,0.007222996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006653931,"threshold_uncertainty_score":0.01323038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.123959240354551,"score_gpt":0.3953626129659423,"score_spread":0.2714033726113913,"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."}}