Research No – 1
Third Ventricular Ependymoma Mimicking Foster Kennedy Syndrome- A Case Report
Aims: To report a case of a third ventricular tumour mimicking Foster Kennedy Syndrome in a young adult.
Presentation of Case: A 21-year-old female presented with bilateral blurring of vision with preceded by generalized headache, nausea and vomiting. Fundoscopy revealed optic nerve atrophy of the right eye and a swollen optic disc on the left in keeping with features of Foster Kennedy Syndrome. MRI of the brain revealed a third ventricular tumor extending into the suprasellar region with hydrocephalus. Surgical excision of the tumour was done and the subsequent histopathological report confirmed it to be a clear cell ependymoma.
Discussion: Foster Kennedy syndrome is a rare clinical constellation describing a pattern of ocular findings typically related to extraaxial tumours involving the anterior skull base. It is characterized by the triad of unilateral optic disc swelling, contralateral optic atrophy and ipsilateral anosmia. The clinical signs of Foster Kennedy syndrome are a result of direct compression of the mass on the optic nerve and an indirect effect from raised intracranial pressure.
Conclusions: We conclude from this report that intraventricular or intraaxial lesions in the vicinity of the optic apparatus may also produce features mimicking Foster Kennedy syndrome in clinical practice.
Research No – 2
Imaging Appearances, Diagnosis and Treatment of Atypical Brain Abscesses: Review of the Literature
Introduction: Atypical brain abscesses mostly occur in immuno-compromised patients especially in various endemic regions of the world. The atypical imaging appearances as well as other diagnostic difficulties cause delays in making diagnoses and hence prognosis is generally very poor. Outcomes can be improved if the clinician has a high index of suspicion in a patient with positive risk factors and suggestive radiological appearances. This will enable early institution of appropriate therapy to improve outcomes.
Methods: A review of existing English literature was done by performing a PubMed search. The various imaging appearances of atypical brain abscesses are described and recommendations made to aid early diagnosis and treatment of atypical brain abscesses.
Results and Discussion: The clinical features of atypical brain abscesses are mostly insidious and non- specific and occur frequently with a medical background of obvious or latent immunodeficiency. The imaging appearances of atypical brain abscesses including Brain CT and MRI scans can be very atypical and non-specific but with the application of modalities like Diffusion Weighted Imaging (DWI) and MR Spectroscopy, atypical brain abscesses can be differentiated from pyogenic bacterial brain abscesses, granulomas and brain tumours.
Microbiological identification has also progressed with advances in molecular microbiology, nuclear medicine and immunology, making differentiation of the various causative organisms of atypical brain abscesses possible and more readily.
Clinical management relies upon early surgical drainage or excision and early use of intravenous antimicrobial agents adapted to the strains identified. Most fungal species are susceptible to Amphotericin B, Voriconazole, Caspofungin, Itraconazole and to a lesser extent Fluconazole. Nocardia species are treated with Cotrimoxazole, Amikacin and Linezolid whilst Toxoplasmosis can be successfully treated with Pyrimethamine and Sulphadiazine or Clindamycin. Actinomyces abscess can be treated with Penicillins and Mycobacterium abscesses are treated with antituberculous agents- Isoniazid, Pyrizinamide, Rifampicin and Ethambutol. Adjuncts to therapy include CSF diversion, corticosteroid and antiseizure medications.
Conclusion: A high index of suspicion, careful reviews of radiological images, early pus/abscess wall samples obtained by drainage, biopsy or surgical resection are needed to establish a definitive microbiological diagnosis and prompt administration of appropriate antimicrobial agents will improve outcomes of atypical brain abscesses.
Research No – 3
DEVELOPING A NUMERICAL SIMULATION OF VASCULAR BRAIN TUMOR GROWTH USING 1-DIMENSIONAL PARTIAL DIFFERENTIAL EQUATION
In this paper a model of vascular brain tumor is developed and solved using Adomian Decomposition Method. The model is formulated as a set of partial differential equations describing the spatial-temporal changes in cell concentrations based on diffusion dynamics. The model predicts the radius of the tumor within certain time schedules. It is formulated in one dimension whereby the tumor is assumed to be growing in radial symmetry. Under this algorithm, equation is decomposed into a series of Adomian polynomials. The model predicts the radius of the tumor at any time schedule after vascularization without necessarily imaging. Results obtained from the simulation of growth and dynamics of malignant brain tumor (GBM) compares well with those from medical literature hence can provide clinical practitioners with valuable information on the potential effects of therapies in their exact schedules.
Research No – 4
DNA methylation-based classification of central nervous system tumours
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging—with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.