A Rare Case of Pancreatoblastoma with Intracranial Seeding.

BACKGROUND: Pancreatoblastoma is an extremely rare neoplasm that accounts for 0.5% of all pancreatic exocrine tumors. These rare entities typically manifest in the pediatric population but can rarely occur in adults. Systemic seeding has been described before but intracranial metastasis in adults has yet to be described. CASE DESCRIPTION: A 28-year-old woman with a history of pancreatoblastoma that had been in remission for 51 months after treatment with cisplatin, doxorubicin (Adriamycin), and etoposide had presented to the emergency room with chronic recurrent headaches. Conservative management of the headaches failed, which led to a diagnostic workup with magnetic resonance imaging of the brain. Magnetic resonance imaging demonstrated a well-circumscribed solitary cerebellar lesion. Metastatic disease was suspected, and the patient underwent suboccipital craniotomy for tumor resection with adjuvant gamma knife radiosurgery. CONCLUSIONS: Central nervous system seeding of pancreatoblastoma is rare, and the available evidence suggests that the strategy we used could be adequate for treating such occurrences.

Multimodal MR imaging model to predict tumor infiltration in patients with gliomas.

INTRODUCTION: Gliomas remain difficult to treat, in part, due to our inability to accurately delineate the margins of the tumor. The goal of our study was to evaluate if a combination of advanced MR imaging techniques and a multimodal imaging model could be used to predict tumor infiltration in patients with diffuse gliomas. METHODS: Institutional review board approval and written consent were obtained. This prospective pilot study enrolled patients undergoing stereotactic biopsy for a suspected de novo glioma. Stereotactic biopsy coordinates were coregistered with multiple standard and advanced neuroimaging sequences in 10 patients. Objective imaging values were assigned to the biopsy sites for each of the imaging sequences. A principal component analysis was performed to reduce the dimensionality of the imaging dataset without losing important information. A univariate analysis was performed to identify the statistically relevant principal components. Finally, a multivariate analysis was used to build the final model describing nuclear density. RESULTS: A univariate analysis identified three principal components as being linearly associated with the observed nuclear density (p values 0.021, 0.016, and 0.046, respectively). These three principal component composite scores are predominantly comprised of DTI (mean diffusivity or average diffusion coefficient and fractional anisotropy) and PWI data (rMTT, Ktrans). The p value of the model was <0.001. The correlation between the predicted and observed nuclear density was 0.75. CONCLUSION: A multi-input, single output imaging model may predict the extent of glioma invasion with significant correlation with histopathology.