S Perreault1, V Ramaswamy2, A S Achrol3, K Chao3, T T Liu4, D Shih2, M Remke2, S Schubert5, E Bouffet6, P G Fisher5, S Partap5, H Vogel7, M D Taylor2, Y J Cho5, K W Yeom8. 1. From the Department of Neurology (S. Perreault, S.S., P.G.F., S. Partap, Y.J.C.), Division of Child NeurologyDivision of Child Neurology (S. Perreault), Centre Hospitalier Universitaire Sainte-Justine, Montreal, Quebec, Canada. 2. Division of Neurosurgery (V.R., D.S., M.R., M.D.T.)Labatt Brain Tumour Research Centre (V.R., D.S., M.R., E.B., M.D.T.)Department of Laboratory Medicine and Pathobiology (V.S., D.S., M.R., M.D.T.), University of Toronto, Toronto, Ontario, Canada. 3. Department of Neurosurgery (A.S.A., K.C.). 4. Department of Radiology (T.T.L.). 5. From the Department of Neurology (S. Perreault, S.S., P.G.F., S. Partap, Y.J.C.), Division of Child Neurology. 6. Labatt Brain Tumour Research Centre (V.R., D.S., M.R., E.B., M.D.T.)Division of Pediatric Hematology/Oncology (E.B), Hospital for Sick Children, Toronto, Ontario, Canada. 7. Richard M. Lucas Center for Imaging, and Department of Pathology (H.V.), Stanford University, Stanford, California. 8. Department of Radiology (K.W.Y.), Lucile Packard Children's Hospital at Stanford University, Palo Alto, California kyeom@stanford.edu.
Abstract
BACKGROUND AND PURPOSE: Recently identified molecular subgroups of medulloblastoma have shown potential for improved risk stratification. We hypothesized that distinct MR imaging features can predict these subgroups. MATERIALS AND METHODS: All patients with a diagnosis of medulloblastoma at one institution, with both pretherapy MR imaging and surgical tissue, served as the discovery cohort (n = 47). MR imaging features were assessed by 3 blinded neuroradiologists. NanoString-based assay of tumor tissues was conducted to classify the tumors into the 4 established molecular subgroups (wingless, sonic hedgehog, group 3, and group 4). A second pediatric medulloblastoma cohort (n = 52) from an independent institution was used for validation of the MR imaging features predictive of the molecular subtypes. RESULTS: Logistic regression analysis within the discovery cohort revealed tumor location (P < .001) and enhancement pattern (P = .001) to be significant predictors of medulloblastoma subgroups. Stereospecific computational analyses confirmed that group 3 and 4 tumors predominated within the midline fourth ventricle (100%, P = .007), wingless tumors were localized to the cerebellar peduncle/cerebellopontine angle cistern with a positive predictive value of 100% (95% CI, 30%-100%), and sonic hedgehog tumors arose in the cerebellar hemispheres with a positive predictive value of 100% (95% CI, 59%-100%). Midline group 4 tumors presented with minimal/no enhancement with a positive predictive value of 91% (95% CI, 59%-98%). When we used the MR imaging feature-based regression model, 66% of medulloblastomas were correctly predicted in the discovery cohort, and 65%, in the validation cohort. CONCLUSIONS: Tumor location and enhancement pattern were predictive of molecular subgroups of pediatric medulloblastoma and may potentially serve as a surrogate for genomic testing.
BACKGROUND AND PURPOSE: Recently identified molecular subgroups of medulloblastoma have shown potential for improved risk stratification. We hypothesized that distinct MR imaging features can predict these subgroups. MATERIALS AND METHODS: All patients with a diagnosis of medulloblastoma at one institution, with both pretherapy MR imaging and surgical tissue, served as the discovery cohort (n = 47). MR imaging features were assessed by 3 blinded neuroradiologists. NanoString-based assay of tumor tissues was conducted to classify the tumors into the 4 established molecular subgroups (wingless, sonic hedgehog, group 3, and group 4). A second pediatric medulloblastoma cohort (n = 52) from an independent institution was used for validation of the MR imaging features predictive of the molecular subtypes. RESULTS: Logistic regression analysis within the discovery cohort revealed tumor location (P < .001) and enhancement pattern (P = .001) to be significant predictors of medulloblastoma subgroups. Stereospecific computational analyses confirmed that group 3 and 4 tumors predominated within the midline fourth ventricle (100%, P = .007), wingless tumors were localized to the cerebellar peduncle/cerebellopontine angle cistern with a positive predictive value of 100% (95% CI, 30%-100%), and sonic hedgehog tumors arose in the cerebellar hemispheres with a positive predictive value of 100% (95% CI, 59%-100%). Midline group 4 tumors presented with minimal/no enhancement with a positive predictive value of 91% (95% CI, 59%-98%). When we used the MR imaging feature-based regression model, 66% of medulloblastomas were correctly predicted in the discovery cohort, and 65%, in the validation cohort. CONCLUSIONS:Tumor location and enhancement pattern were predictive of molecular subgroups of pediatric medulloblastoma and may potentially serve as a surrogate for genomic testing.
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