{"id":"W4224222733","doi":"10.1148/radiol.212137","title":"MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study","year":2022,"lang":"en","type":"article","venue":"Radiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; SickKids Foundation; Hospital for Sick Children","funders":"National Cancer Institute; Cancer Research UK; National Institutes of Health; Brain Tumour Charity; Canadian Institutes of Health Research; National Institute for Health and Care Research; American Brain Tumor Association","keywords":"Radiogenomics; Medicine; Medulloblastoma; Mann–Whitney U test; Classifier (UML); Artificial intelligence; Receiver operating characteristic; Binary classification; Machine learning; Oncology; Internal medicine; Pathology; Radiology; Radiomics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001213129,0.0004474997,0.0004437472,0.0008620966,0.0004203143,0.0004382055,0.0004132607,0.0003192934,0.0006767997],"category_scores_gemma":[0.002501447,0.0002864797,0.0004408069,0.0009660067,0.0003374207,0.0004501894,0.0004384178,0.0003578832,0.0002152587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006343204,"about_ca_system_score_gemma":0.0005282691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00407089,"about_ca_topic_score_gemma":0.003992929,"domain_scores_codex":[0.9992999,0.0002294823,0.00006301146,0.000226192,0.0001062192,0.00007509282],"domain_scores_gemma":[0.9983187,0.0002512896,0.0007919648,0.0002164166,0.0002001893,0.0002215141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002408306,0.000066993,0.9970151,0.0000083362,0.00006556373,0.0001212661,0.00009369394,0.00005494374,0.0003978327,0.00001114107,0.00007441534,0.001849961],"study_design_scores_gemma":[0.00002896461,0.0004273337,0.9975752,0.00000580809,0.0000727823,0.0008586952,0.0002349708,0.0002860272,0.0002208488,0.00001243209,0.0002726211,0.000004345845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996266,0.00007067002,0.00006324582,0.00001101265,8.861622e-7,0.00001117269,0.000157774,0.000002023355,0.00005661911],"genre_scores_gemma":[0.999127,0.0001046946,0.0002072655,0.00001309105,0.00000881661,0.00002471515,0.0004795909,0.000003704196,0.0000310615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00407089,"threshold_uncertainty_score":0.00809437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009778225191930154,"score_gpt":0.2794301015588814,"score_spread":0.2696518763669512,"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."}}