Longitudinal MRI assessment: the identification of relevant features in the development of posterior fossa syndrome in children

Up to 25% of children who undergo brain tumour resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterised 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 lobes 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. Intra-operative MRI (IoMRI) is used during surgical procedures at the Alder Hey Children’s Hospital, Liverpool, England, in the treatment of Posterior Fossa tumours and allows visualisation of the brain during surgery. The final MR scan on the IoMRI allows early assessment of the ION immediately after the surgical procedure. The longitudinal MRI data of 28 patients was analysed in a collaborative study with Alder Hey Children’s Hospital, in order to identify the most relevant imaging features that relate to the development of PFS, specifically related to HOD. A semi-automated segmentation process was carried out to delineate the ION on each MRI. Feature selection techniques were used to identify the most relevant features amongst the MRI data, demographics and clinical data provided by the hospital. A support vector machine (SVM) was used to analyse the discriminative ability of the selected features. The results indicate the presence of HOD as the most efficient feature that correlates with the development of PFS, followed by the change in intensity and size of the ION and whether HOD occurred bilaterally or unilaterally

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 (p=0.01) when using this feature alone

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

Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials

The diffusivity of water in aqueous cesium iodide solutions is larger than that in neat liquid water, and vice versa for sodium chloride solutions. Such peculiar ion-specific behavior, called anomalous diffusion, is not reproduced in typical force field-based molecular dynamics (MD) simulations due to inadequate treatment of ion-water interactions. Herein, this hurdle is tackled using machine learned atomic potentials (MLPs) trained on data from density functional theory calculations. MLP-based atomistic MD simulations of aqueous salt solutions reproduce experimentally determined thermodynamic, structural, dynamical, and transport properties, including their varied trends of water diffusivities across salt concentration. This enables an examination of their intermolecular structure to unravel the microscopic underpinnings of the distinction in their transport. While both ions in CsI solutions contribute to faster diffusion of water molecules, the competition between the heavy retardation by Na-ions and slight acceleration by Cl-ions in NaCl solutions reduces their water diffusivity.Comment: 23 pages, 5 figure

Quantitative measurement of blood flow in paediatric brain tumours. A comparative study of dynamic susceptibility contrast and multi-timepoint arterial spin-labelled MRI

OBJECTIVE: Arterial spin-labelling (ASL) MRI uses intrinsic blood water to quantify the cerebral blood flow (CBF), removing the need for the injection of a gadolinium-based contrast agent used for conventional perfusion imaging such as dynamic susceptibility contrast (DSC). Owing to the non-invasive nature of the technique, ASL is an attractive option for use in paediatric patients. This work compared DSC and multi-timepoint ASL measures of CBF in paediatric brain tumours. METHODS: Patients (n = 23; 20 low-grade tumours and 3 high-grade tumours) had DSC and multi-timepoint ASL with and without vascular crushers (VC). VC removes the contribution from larger vessel blood flow. Mean perfusion metrics were extracted from control and T(1)-enhanced tumour regions of interest (ROIs): arterial arrival time (AAT) and CBF from the ASL images with and without VC, relative cerebral blood flow (rCBF), relative cerebral blood volume, delay time (DT) and mean transit time (MTT) from the DSC images. RESULTS: Significant correlations existed for: AAT and DT (r = 0.77, p = 0.0002) and CBF and rCBF (r = 0.56, p = 0.02) in control ROIs for ASL-noVC. No significant correlations existed between DSC and ASL measures in the tumour region. Significant differences between control and tumour ROI were found for MTT (p < 0.001) and rCBF (p < 0.005) measures. CONCLUSION: Significant correlations between ASL-noVC and DSC measures in the normal brain suggest that DSC is most sensitive to macrovascular blood flow. The absence of significant correlations within the tumour ROI suggests that ASL is sensitive to different physiological mechanisms compared with DSC measures. ADVANCES IN KNOWLEDGE: ASL provides information which is comparable with that of DSC in healthy tissues, but appears to reflect a different physiology in tumour tissues

Neurocognitive dysfunction after treatment for pediatric brain tumors:Subtype-specific findings and proposal for brain network-informed evaluations

The increasing number of long-term survivors of pediatric brain tumors requires us to incorporate the most recent knowledge derived from cognitive neuroscience into their oncological treatment. As the lesion itself, as well as each treatment, can cause specific neural damage, the long-term neurocognitive outcomes are highly complex and challenging to assess. The number of neurocognitive studies in this population grows exponentially worldwide, motivating modern neuroscience to provide guidance in follow-up before, during and after treatment. In this review, we provide an overview of structural and functional brain connectomes and their role in the neuropsychological outcomes of specific brain tumor types. Based on this information, we propose a theoretical neuroscientific framework to apply appropriate neuropsychological and imaging follow-up for future clinical care and rehabilitation trials

Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity

Shear viscosity, though being a fundamental property of all liquids, is computationally expensive to estimate from equilibrium molecular dynamics simulations. Recently, Machine Learning (ML) methods have been used to augment molecular simulations in many contexts, thus showing promise to estimate viscosity too in a relatively inexpensive manner. However, ML methods face significant challenges like overfitting when the size of the data set is small, as is the case with viscosity. In this work, we train several ML models to predict the shear viscosity of a Lennard-Jones (LJ) fluid, with particular emphasis on addressing issues arising from a small data set. Specifically, the issues related to model selection, performance estimation and uncertainty quantification were investigated. First, we show that the widely used performance estimation procedure of using a single unseen data set shows a wide variability on small data sets. In this context, the common practice of using Cross validation (CV) to select the hyperparameters (model selection) can be adapted to estimate the generalization error (performance estimation) as well. We compare two simple CV procedures for their ability to do both model selection and performance estimation, and find that k-fold CV based procedure shows a lower variance of error estimates. We discuss the role of performance metrics in training and evaluation. Finally, Gaussian Process Regression (GPR) and ensemble methods were used to estimate the uncertainty on individual predictions. The uncertainty estimates from GPR were also used to construct an applicability domain using which the ML models provided more reliable predictions on another small data set generated in this work. Overall, the procedures prescribed in this work, together, lead to robust ML models for small data sets.Comment: main: 17 pages, 11 figures ; SI: 55 pages, 29 figures ; to be submitted to Journal of Chemical Physic

STATISTICS OF CANCER, 2020 IN INDIAN STATES: A REVIEW ON THE REPORT FROM NATIONAL CANCER REGISTRY PROGRAMME

The deliberate assortment of information on cancer growth was performed by different populace-based disease vaults (population-based cancer registries [PBCRs]) and clinic-based cancer growth libraries (hospital-based cancer registries [HBCRs]) across India under the National Cancer Registry Program–National Center for Disease Informatics and Research of Indian Council of Medical Research since 1982. This survey analyzed the malignant growth occurrence, designs, patterns, projections, and mortality from 28 PBCRs and furthermore the stage at introduction and kind of therapy of patients with disease from 58 HBCRs (n=667,666) from the pooled investigation for the composite time frame 2012–2016. Time patterns in cancer growth rate were created as yearly percent change from 16 PBCRs (those with at least 10 years of consistent great information accessible) utilizing Joinpoint relapse. Aizawl locale (269.4) and Papumpare region (219.8) had the most elevated age changed occurrence rates among guys and females, separately. The extended number of patients with disease in India is 1,392,179 for the year 2020, and the basic five driving destinations are cancer, lung, mouth, cervix uteri, and tongue. Patterns in disease frequency rate showed an expansion on the whole locales of cancer in both genders and were high in Kamrup Metropolitan (yearly percent change, 3.8%; p&lt;0.05). Most of the patients with cancer were analyzed at the privately progressed stage for cancer (57.0%), cervix uteri (60.0%), head and neck (66.6%), and stomach (50.8%) disease, while in cellular breakdown in the lungs, far off metastasis was dominating among guys (44.0%) and females (47.6%). This audit gives a system to surveying the status and patterns of cancer growth in India. It will manage proper help for activity to fortify endeavors to improve cancer growth avoidance and control to accomplish the public non-communicable illness targets and the reasonable advancement objectives

Locked-In with COVID-19

© 2020 Elsevier Ltd Coronavirus Disease 2019 (COVID-19) can be associated with various neurological manifestations including acute strokes. Hyper acute diagnosis and treatment are key factors which decrease mortality and morbidity in stroke patients. The COVID-19 pandemic has introduced a great strain on the healthcare system, and as a result clinicians are facing several barriers in diagnosing and treating strokes. Delayed presentation of strokes is a problem as some in the general population defer the decision to seek immediate medical attention fearing contracting the virus. Also playing a role is the paucity of healthcare professionals available during a pandemic. Recent literature demonstrates the association of acute strokes in young patients with COVID-19. Lack of clear pathophysiology of the neurological manifestations from COVID-19 intensifies the problem. A thorough examination of the intensive care unit patient has always been a challenge owing to several factors including use of sedatives, sepsis, uremia, and encephalopathy secondary to medications. Locked-In Syndrome (LIS) secondary to stroke is much more challenging to diagnose as patients are unable to communicate or elicit any motor functions apart from certain ocular movements. We present the case of a 25 year old patient with no known history of coagulopathy, but had developed COVID-19 cytokine storm which culminated in LIS secondary to pontine strokes

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. Keywords: Cerebellar mutism syndrome (CMS); hypertrophic olivary degeneration; posterior fossa syndrome (PFS); postoperative pediatric cerebellar mutism syndrom