PT - JOURNAL ARTICLE AU - S. Perreault AU - V. Ramaswamy AU - A.S. Achrol AU - K. Chao AU - T.T. Liu AU - D. Shih AU - M. Remke AU - S. Schubert AU - E. Bouffet AU - P.G. Fisher AU - S. Partap AU - H. Vogel AU - M.D. Taylor AU - Y.J. Cho AU - K.W. Yeom TI - MRI Surrogates for Molecular Subgroups of Medulloblastoma AID - 10.3174/ajnr.A3990 DP - 2014 Jul 01 TA - American Journal of Neuroradiology PG - 1263--1269 VI - 35 IP - 7 4099 - http://www.ajnr.org/content/35/7/1263.short 4100 - http://www.ajnr.org/content/35/7/1263.full SO - Am. J. Neuroradiol.2014 Jul 01; 35 AB - 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. CP/CPAcerebellar peduncle/cerebellopontine angle cisternFSLfMRI of the Brain Software LibrarySHHsonic hedgehogWNTwingless