{"id":"W2955569215","doi":"10.1016/j.wneu.2019.08.232","title":"Noninvasive O6 Methylguanine-DNA Methyltransferase Status Prediction in Glioblastoma Multiforme Cancer Using Magnetic Resonance Imaging Radiomics Features: Univariate and Multivariate Radiogenomics Analysis","year":2019,"lang":"en","type":"article","venue":"World Neurosurgery","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Kermanshah University of Medical Sciences; Iran University of Medical Sciences","keywords":"Radiogenomics; Univariate; Medicine; Magnetic resonance imaging; Feature selection; Receiver operating characteristic; Artificial intelligence; Univariate analysis; Multivariate analysis; Oncology; Multivariate statistics; Radiology; Radiomics; Internal medicine; Machine learning; 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":[0.0006092989,0.0004147998,0.0004550694,0.0007651785,0.0001667464,0.0005404223,0.0002131158,0.0002951383,0.0005780443],"category_scores_gemma":[0.001321624,0.0001136537,0.0004898032,0.000440057,0.0002188977,0.0003409451,0.0003253803,0.0003880848,0.0001301203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002454032,"about_ca_system_score_gemma":0.0003071586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00228762,"about_ca_topic_score_gemma":0.003851823,"domain_scores_codex":[0.9998009,0.00005551456,0.00001655967,0.00004313577,0.00004150578,0.00004244086],"domain_scores_gemma":[0.9995621,0.0001550041,0.0001071085,0.00003549685,0.0000621674,0.00007807153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001214402,0.0001509517,0.942057,0.00002942507,0.0002472169,0.0001377385,0.00006624468,0.001767945,0.0118596,0.00008393615,0.0004050411,0.04198036],"study_design_scores_gemma":[0.00003205539,0.0005760813,0.9489805,0.00001209117,0.0004663078,0.0005557058,0.0002763736,0.04122557,0.006543489,0.0005286414,0.0007720923,0.00003115505],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982359,0.000244672,0.00104818,0.00006618076,0.000007709102,0.000004248893,0.0001957758,0.00001977086,0.0001775394],"genre_scores_gemma":[0.9993277,0.00006739172,0.0002752868,0.000008337261,0.0000116764,0.000002649949,0.000179853,0.000003901608,0.000123229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00228762,"threshold_uncertainty_score":0.004548609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009762077671919188,"score_gpt":0.2677392202106624,"score_spread":0.2579771425387433,"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."}}