{"id":"W2809364338","doi":"10.1007/s11517-018-1858-4","title":"Prediction of survival with multi-scale radiomic analysis in glioblastoma patients","year":2018,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":86,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; McGill University Health Centre","funders":"","keywords":"Random forest; Radiomics; Univariate; Glioblastoma; Artificial intelligence; Fluid-attenuated inversion recovery; Correlation; Correlation coefficient; Rank correlation; Pattern recognition (psychology); Progression-free survival; Estimator; Medicine; Mathematics; Computer science; Multivariate statistics; Statistics; Magnetic resonance imaging; Oncology; Overall survival; Radiology","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":[0.0004900871,0.0003510668,0.000387478,0.0009829253,0.0002133204,0.0006236319,0.0002312291,0.0003962629,0.0008006674],"category_scores_gemma":[0.001872403,0.0001098596,0.0004839868,0.0004912725,0.0001734793,0.0004175593,0.0004871984,0.0002667099,0.0002418161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002643818,"about_ca_system_score_gemma":0.0001880042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001876138,"about_ca_topic_score_gemma":0.002388967,"domain_scores_codex":[0.9998044,0.00006166068,0.00002336134,0.000045055,0.00002846601,0.00003702717],"domain_scores_gemma":[0.9993353,0.0001919817,0.0002158049,0.00004976899,0.0001040551,0.0001029809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005953205,0.00003233341,0.9875145,0.00001059252,0.00009944217,0.00009508704,0.00003060358,0.001256327,0.0007362649,0.0000287062,0.0002302517,0.009370513],"study_design_scores_gemma":[0.00001364,0.0003181864,0.9777965,0.00001113289,0.0002113926,0.0003663279,0.000246307,0.01969679,0.0006331897,0.0002830743,0.0004057202,0.00001774352],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983721,0.0002619343,0.0006379406,0.00006468747,0.00001461328,0.000005676334,0.0002796398,0.0000159584,0.0003473953],"genre_scores_gemma":[0.999474,0.00005649602,0.0001294237,0.000006161894,0.000009786921,0.000003479787,0.0002325744,0.000002048967,0.00008620177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001876138,"threshold_uncertainty_score":0.003730416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01216833625535062,"score_gpt":0.252875117010992,"score_spread":0.2407067807556414,"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."}}