Anil De Silva

Rickettsial infection is an important differential diagnosis in prolonged febrile illness and in returned travellers.We report a 44-year-old male from Deraniyagala, Sri Lanka, with spotted fever Rickettsiosis presenting with stroke and Guillain-Barre syndrome. He presented with dysarthria, reduced sensorium, and acute renal failure on the background of a prolonged febrile illness over two weeks. Following admission there was rapid deterioration of consciousness due to renal failure requiring intubation and repeated haemodialysis. On brain imaging, there were bilateral basal ganglia and internal and external capsule infarctions. His serology was positive for spotted fever Rickettsial infection and there was rapid improvement following treatment with doxycycline. However, difficulty in weaning off from the ventilator due to poor respiratory effort and symmetrical flaccid quadriparesis with areflexia raised concerns of concomitant Guillain-Barre syndrome. Serial cerebrospinal fluid (CSF) studies showed the development of elevated CSF protein with cytoalbuminologic dissociation during his stay in the intensive care unit (ICU). A diagnosis of co-existing Guillain-Barre syndrome was supported by the neuro-physiological findings and marked motor improvement following therapeutic plasmapheresis. This unmasked the right-sided mild residual hemiparesis he suffered due to stroke.

Background and objectives- Multiple Sclerosis (MS) and Neuromyelitis Optica Spectrum Disorders (NMOSD) are the commonest demyelinating diseases of the central nervous system with overlapping clinical and radiological features, leading to frequent misinterpretation. This study aimed to describe neuroimaging features for differentiating MS and NMOSD and also comparing NMOSD subcategories.Methods- A cross sectional study was conducted at a single tertiary referral center in Sri Lanka. The magnetic resonance imaging (MRI) characteristics of 82 MS and 36 NMOSD patients were prospectively evaluated by a fourth-year resident under the supervision of consultant radiologists and were analyzed using standard descriptive statistical tests. Results- Statistical analyses indicated significance (p<0.05) in distinguishing both entities (MS and 36 NMOSD) across lesions in periventricular, subcortical, intracortical, inferior temporal, corpus callosum, and infratentorial locations by MRI. Dawson finger lesions, U fibers, calloso-septal interface involvement and T1 black holes indicated MS, whereas linear ependymal lesions suggested NMOSD. In the spinal cord, peripherally located short-segment lesions were significant for MS (p=0.000) and longitudinally extensive lesions involving central grey matter (p=0.000) pointed towards NMOSD. There was only a slight variation in lesion distribution between cervical and thoracic cord. MS showed more brain parenchymal volume loss while spinal cord atrophy was higher in NMOSD. Symmetry and location of the optic pathway lesions reinforced the differentiation.Conclusion- Comprehensive analyses support the use of neuroimaging in distinguishing between NMOSD and MS. However, further multicentric longitudinal studies are required to differentiate NMOSD phenotypes.

Background: Apparent Diffusion Coefficient (ADC) of Magnetic Resonance Imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using Diffusion-weighted imaging (DWI). This study focuses on developing a robust Machine Learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and Grey Level Co-occurrence Matrix (GLCM) texture features of ADC. Methods: A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. Results: According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772±0.0237) performed the highest score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. Conclusion: The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.

Background: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors. Methods: This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed. Results: According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process. Conclusions: This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.