Abstract
Post-operative cerebellar mutism syndrome (POPCMS) in children is a post- surgical complication which occurs following the resection of tumors within the brain stem and cerebellum. High resolution brain magnetic resonance (MR) images acquired at multiple time points across a patient’s treatment allow the quantification of localized changes caused by the progression of this syndrome. However, MR images are not necessarily acquired at regular intervals throughout treatment and are often not volumetric. This restricts the analysis to 2D space and causes difficulty in intra- and inter-subject comparison. To address these challenges, we have developed an automated image processing and analysis pipeline. Multi-slice 2D MR image slices are interpolated in space and time to produce a 4D volumetric MR image dataset providing a longitudinal representation of the cerebellum and brain stem at specific time points across treatment. The deformations within the brain over time are represented using a novel metric known as the Jacobian of deformations determinant. This metric, together with the changing grey-level intensity of areas within the brain over time, are analyzed using machine learning techniques in order to identify biomarkers that correspond with the development of POPCMS following tumor resection. This study makes use of a fully automated approach which is not hypothesis-driven. As a result, we were able to automatically detect six potential biomarkers that are related to the development of POPCMS following tumor resection in the posterior fossa.
Highlights
Introduction and BackgroundPost-operative cerebellar mutism syndrome (POPCMS) is a post-surgical complication whose exact underlying pathophysiological mechanism is unclear
The patients were aged between 8 months and 18 years of age, seven of whom were diagnosed with POPCMS, the diagnosis of which was made by a neurologist following the review of clinical documentation
Image; the same slice with the cerebellum and brain stem probability map highlighted in red; and the resultant isolated cerebellum
Summary
Post-operative cerebellar mutism syndrome (POPCMS) is a post-surgical complication whose exact underlying pathophysiological mechanism is unclear. The aim of this study is to identify biomarkers for POPCMS following tumor resection surgery in the posterior fossa, and to do so through a fully automated process which is not hypothesis drive. In order to do so, changes in grey-level intensity within distinct lobules in the brain stem and the cerebellum were analyzed longitudinally. Morphological change within these lobules following tumor resection, which is thought to contribute to the patient’s recovery after surgery, was analyzed in this way. The fully-automated pipeline presented in this paper may be applied to similar longitudinal analysis problems
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
