The role of early intra-operative MRI in partial resection of optic pathway/hypothalamic gliomas in children.

INTRODUCTION: Optic pathway/hypothalamic gliomas (OPHGs) are generally benign but situated in an exquisitely sensitive brain region. They follow an unpredictable course and are usually impossible to resect completely. We present a case series of 10 patients who underwent surgery for OPHGs with the aid of intra-operative MRI (ioMRI). The impact of ioMRI on OPHG resection is presented, and a role for ioMRI in partial resection is discussed. METHODS: Ten patients with OPHGs managed surgically utilising ioMRI at Alder Hey Children's Hospital between 2010 and 2013 were retrospectively identified. Demographic and relevant clinical data were obtained. MRI was used to estimate tumour volume pre-operatively and post-resection. If ioMRI demonstrated that further resection was possible, second-look surgery, at the discretion of the operating surgeon, was performed, followed by post-operative imaging to establish the final status of resection. Tumour volume was estimated for each MR image using the MRIcron software package. RESULTS: Control of tumour progression was achieved in all patients. Seven patients had, on table, second-look surgery with significant further tumour resection following ioMRI without any surgically related mortality or morbidity. The median additional quantity of tumour removed following second-look surgery, as a percentage of the initial total volume, was 27.79% (range 11.2-59.2%). The final tumour volume remaining with second-look surgery was 23.96 vs. 33.21% without (p = 0.1). CONCLUSIONS: OPHGs are technically difficult to resect due to their eloquent location, making them suitable for debulking resection only. IoMRI allows surgical goals to be reassessed intra-operatively following primary resection. Second-look surgery can be performed if possible and necessary and allows significant quantities of extra tumour to be resected safely. Although the clinical significance of additional tumour resection is not yet clear, we suggest that ioMRI is a safe and useful additional tool, to be combined with advanced neuronavigation techniques for partial tumour resection.

Fully-Automated Identification of Imaging Biomarkers for Post-Operative Cerebellar Mutism Syndrome Using Longitudinal Paediatric MRI.

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.

Facial changes related to brachycephaly in Cavalier King Charles Spaniels with Chiari-like malformation associated pain and secondary syringomyelia.

BACKGROUND: Recent studies including an innovative machine learning technique indicated Chiari-like malformation (CM) is influenced by brachycephalic features. OBJECTIVES: Morphometric analysis of facial anatomy and dysmorphia in CM-associated pain (CM-P) and syringomyelia (SM) in the Cavalier King Charles Spaniel (CKCS). ANIMALS: Sixty-six client-owned CKCS. METHODS: Retrospective study of anonymized T2W sagittal magnetic resonance imaging of 3 clinical groups: (1) 11 without central canal dilation (ccd) or SM (CM-N), (2) 15 with CM-P with no SM or <2 mm ccd (CM-P), and (3) 40 with syrinx width ≥4 mm (SM-S). Morphometric analysis assessed rostral skull flattening and position of the hard and soft palate relative to the cranial base in each clinical group and compared CKCS with and without SM-S. RESULTS: Sixteen of 28 measured variables were associated to SM-S compared to CM-N and CM-P. Of these 6 were common to both groups. Predictive variables determined by discriminant analysis were (1) the ratio of cranial height with cranial length (P < .001 between SM-S and CM-N) and (2) the distance between the cerebrum and the frontal bone (P < .001 between SM-S and CM-P). CM-P had the lowest mean height of the maxillary area. CONCLUSIONS AND CLINICAL IMPORTANCE: CKCS with CM-P and SM-S have cranial brachycephaly with osseous insufficiency in the skull with rostral flattening and increased proximity of the hard and soft palate to the cranial base. Changes are greatest with CM-P. These findings have relevance for understanding disease pathogenesis and for selection of head conformation for breeding purposes.

Using machine learning to understand neuromorphological change and image-based biomarker identification in Cavalier King Charles Spaniels with Chiari-like malformation-associated pain and syringomyelia.

BACKGROUND: Chiari-like malformation (CM) is a complex malformation of the skull and cranial cervical vertebrae that potentially results in pain and secondary syringomyelia (SM). Chiari-like malformation-associated pain (CM-P) can be challenging to diagnose. We propose a machine learning approach to characterize morphological changes in dogs that may or may not be apparent to human observers. This data-driven approach can remove potential bias (or blindness) that may be produced by a hypothesis-driven expert observer approach. HYPOTHESIS/OBJECTIVES: To understand neuromorphological change and to identify image-based biomarkers in dogs with CM-P and symptomatic SM (SM-S) using a novel machine learning approach, with the aim of increasing the understanding of these disorders. ANIMALS: Thirty-two client-owned Cavalier King Charles Spaniels (CKCSs; 11 controls, 10 CM-P, 11 SM-S). METHODS: Retrospective study using T2-weighted midsagittal Digital Imaging and Communications in Medicine (DICOM) anonymized images, which then were mapped to images of an average clinically normal CKCS reference using Demons image registration. Key deformation features were automatically selected from the resulting deformation maps. A kernelized support vector machine was used for classifying characteristic localized changes in morphology. RESULTS: Candidate biomarkers were identified with receiver operating characteristic curves with area under the curve (AUC) of 0.78 (sensitivity 82%; specificity 69%) for the CM-P biomarkers collectively and an AUC of 0.82 (sensitivity, 93%; specificity, 67%) for the SM-S biomarkers, collectively. CONCLUSIONS AND CLINICAL IMPORTANCE: Machine learning techniques can assist CM/SM diagnosis and facilitate understanding of abnormal morphology location with the potential to be applied to a variety of breeds and conformational diseases.

Post-operative pediatric cerebellar mutism syndrome and its association with hypertrophic olivary degeneration.

BACKGROUND: The dentato-thalamo-cortical (DTC) pathway is recognized as the anatomical substrate for postoperative pediatric cerebellar mutism (POPCMS), a well-recognized complication affecting up to 31% of children undergoing posterior fossa brain tumour resection. The proximal structures of the DTC pathway also form a segment of the Guillain and Mollaret triangle, a neural network which when disrupted causes hypertrophic olivary degeneration (HOD) of the inferior olivary nucleus (ION). We hypothesize that there is an association between the occurrence of POPCMS and HOD and aim to evaluate this on MR imaging using qualitative and quantitative analysis of the ION in children with and without POPCMS. METHODS: In this retrospective study we qualitatively analysed the follow up MR imaging in 48 children who underwent posterior fossa tumour resection for presence of HOD. Quantitative analysis of the ION was possible in 28 children and was performed using semi-automated segmentation followed by feature extraction and feature selection techniques and relevance of the features to POPCMS were evaluated. The diagnosis of POPCMS was made independently based on clinical and nursing assessment notes. RESULTS: There was significant association between POPCMS and bilateral HOD (P=0.002) but not unilateral HOD. Quantitative analysis showed that hyperintensity in the left ION was the most relevant feature in children with POPCMS. CONCLUSIONS: Bilateral HOD can serve as a reliable radiological indicator in establishing the diagnosis of POPCMS particularly in equivocal cases. The strong association of signal change due to HOD in the left ION suggests that injury to the right proximal efferent cerebellar pathway plays an important role in the causation of POPCMS.

Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI.

Up to 25% of children who undergo brain tumor resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterized by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in structures within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Longitudinal MRI datasets of 28 patients were acquired consisting of pre-, intra-, and postoperative scans. A semiautomated segmentation process was used to segment the ION on each MR image. A full set of imaging features describing the first- and second-order statistics and size of the ION were extracted for each image. Feature selection techniques were used to identify the most relevant features among the MRI features, demographics, and data based on neuroradiological assessment. A support vector machine was used to analyze the discriminative features selected by a generative k-nearest neighbor algorithm. The results indicate the presence of hyperintensity in the left ION as the most diagnostically relevant feature, providing a statistically significant improvement in the classification of patients ([Formula: see text]) when using this feature alone.