Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

Hydrocephalus, a rare manifestation of sarcoidosis.

A 36-week-pregnant woman developed a symptomatic hydrocephalus. Chest imaging showed bihilar lymphadenopathy and histological examination of a mediastinal lymph node revealed non-caseating granulomas. After delivery, her neurologic complaints progressed. Placement of a ventriculoperitoneal drain (VPD) did not reduce the symptoms. However, steroids resulted in rapid disappearance of the hydrocephalus. Hydrocephalus is a very rare manifestation of sarcoidosis. The diagnosis relies on the ability of clinicians to recognize this disorder. This case shows how a difference in opinion of the several specialists involved can lead to a delay in diagnosis and treatment.