Medulloblastoma Magnetic Resonance Imaging

T1WI → hypo- to isointense.

Generally hypointense to grey matter

overall 90% enhance, often heterogeneously Medulloblastoma, WNT-activated tend to vividly enhance ¹⁾

Group 4 medulloblastoma tend to enhance less ²⁾

Tumor location and enhancement pattern were predictive of molecular subgroups of pediatric medulloblastoma and may potentially serve as a surrogate for genomic testing ³⁾.

Enhancing medulloblastomas exhibited strong VEGFR1/2 and CD31 expression relative to nonenhancing tumors. There was no significant difference in perioperative complications or patient survival between the 2 groups.

These results suggest that in patients with medulloblastoma the presence of enhancement on MRI may correlate with increased vascularity and angiogenesis, but does not correlate with worse patient prognosis in the short or long term ⁴⁾.

overall are iso to hyperintense to grey matter

heterogeneous due to calcification, necrosis, and cyst formation

surrounding edema is common ⁵⁾

T2 weighted image → heterogeneous due to tumor cysts, vessels, and calcifications ⁶⁾

high DWI signal (“restricted diffusion”).

elevated choline

decreased NAA.

taurine peak

high creatine

little or no taurine

low creatine.

Low ADC values (lower than normal cerebellum e.g. ~550 x 10-6 mm2/s) ⁷⁾.

Using only apparent diffusion coefficient (ADC) values measured on ADC maps. One hundred and three pediatric patients with pre-operative magnetic resonance imaging scans showing a posterior fossa tumor with histological verification were retrospectively identified from a ten-year period at a tertiary care medical center. A single observer measured the lowest ADC values in all tumors to determine the mean minimum ADC (ADCmin) value that provided greatest accuracy in distinguishing medulloblastomas from other tumors, which was determined to be 0.66×10(-3) mm(2)/s. Imaging studies, including ADC maps, from 90 patients were provided to two neuroradiologists, who provided a diagnosis, which was later dichotomized as medulloblastoma or other. Two medical students measured ADCmin within tumors and those with ADCmin < 0.66×10(-3) mm(2)/s were recorded as medulloblastoma; any other value was recorded as other. Diagnostic accuracy was measured. ADCmin values allowed a correct identification of lesions as either medulloblastoma or other in 91% of cases. After diagnoses by the two neuroradiologists were categorized as either medulloblastoma or other, their diagnoses were correct in 90% and 84% of cases, respectively. In 19 cases, at least one neuroradiologist was incorrect; the addition of ADC values to clinical interpretation would have allowed a correct diagnosis in 63% of such cases. Diagnostic accuracy based on ADC values by medical students was comparable to that of subspecialty-trained neuroradiologists. This findings suggest that the addition of ADC values to standard film interpretation may improve the diagnostic rate for these tumors ⁸⁾.

Both ADCmin and nADC could serve as the basis for a CAD program to distinguish medulloblastoma from other posterior fossa tumors with a high degree of accuracy ⁹⁾.

94 cases, of which 75 were diagnosed cases of ependymoma, medulloblastoma, brainstem glioma, and pilocytic astrocytoma and 19 were normal MRI brain cases. The data was randomized into training data, 64 cases; test data, 21 cases and validation data, 9 cases to devise a deep learning algorithm to segment the pediatric brain tumor. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the deep learning model were compared with the radiologist's findings. Performance evaluation of AS was done based on Dice score and Hausdorff95 distance.

Analysis of MRI semantic features was done with necrosis and hemorrhage as predicting features for ependymoma, diffusion restriction, and cystic changes were predictors for medulloblastoma. The accuracy of detecting abnormalities was 90%, with a specificity of 100%. Further segmentation of the tumor into enhancing and non-enhancing components was done. The segmentation results for whole tumor (WT), enhancing tumor (ET), and non-enhancing tumor (NET) have been analyzed by Dice score and Hausdorff95 distance. The accuracy of the prediction of all MRI features was compared with experienced radiologist's findings. Substantial agreement was observed between the classification by model and the radiologist's given classification [K-0.695 (K is Cohen's kappa score for interrater reliability)].

The deep learning model had very high accuracy and specificity for predicting the magnetic resonance (MR) characteristics and close to 80% accuracy in predicting tumor type. This model can serve as a potential tool to make a timely and accurate diagnosis for radiologists not trained in neuroradiology ¹⁰⁾

As CSF seeding is common at presentation, imaging with the contrast of the whole neuraxis is recommended to identify drop metastases and leptomeningeal spread. Although rare, the extraneural spread is reported.

MRI with IV gadolinium or CT/myelography with water-soluble contrast should be done to rule out “drop metastases.” Staging is done either pre-op or within 2–3 weeks of surgery.