{"id":"W4394855486","doi":"10.3390/curroncol31040165","title":"Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics","year":2024,"lang":"en","type":"article","venue":"Current Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Isocitrate dehydrogenase; Medicine; Radiomics; Proportional hazards model; Glioblastoma; Lasso (programming language); Magnetic resonance imaging; Feature selection; IDH1; Effective diffusion coefficient; Perfusion; Nuclear medicine; Oncology; Radiology; Pathology; Internal medicine; Artificial intelligence; Computer science; Cancer research; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007563024,0.0005657966,0.0003803286,0.001317889,0.00009626742,0.0004863743,0.0001988421,0.0002576207,0.0003420727],"category_scores_gemma":[0.001842213,0.0001250708,0.0003986014,0.0004619778,0.0002493345,0.0003220896,0.0002823725,0.0003082922,0.0001432428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002602891,"about_ca_system_score_gemma":0.0002521562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001153677,"about_ca_topic_score_gemma":0.001929344,"domain_scores_codex":[0.9999012,0.00003547271,0.000008826093,0.00002182476,0.00001888786,0.00001385476],"domain_scores_gemma":[0.9993843,0.0001989454,0.0002455894,0.00004201315,0.00008671413,0.00004248358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009274057,0.0003191173,0.6377071,0.0004174564,0.0004828886,0.0005954611,0.00022143,0.05669954,0.03421339,0.0009053221,0.00235127,0.2651596],"study_design_scores_gemma":[0.00003890018,0.0008043289,0.6869772,0.0001530937,0.0004561653,0.001519924,0.000215844,0.2838002,0.01581614,0.006666032,0.003472001,0.00008018457],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9513972,0.003119554,0.04241765,0.0006503634,0.0000208879,0.00002946621,0.001049454,0.0002108591,0.001104596],"genre_scores_gemma":[0.9931352,0.0005803043,0.005547927,0.0000423515,0.00003535598,0.0000145166,0.0005137536,0.00001058044,0.0001198865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001317889,"threshold_uncertainty_score":0.00399977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0483235910964295,"score_gpt":0.3905166699949984,"score_spread":0.3421930788985689,"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."}}