Predicting the risk of high-grade gliomas in resource-constrained settings

Introduction: Gliomas are the most common primary brain tumours worldwide and can be broadly classified as low-grade or high-grade based on molecular and histopathological features. 
Methodology: A retrospective single-centre single-surgeon unmatched case‒control study was conducted using sociodemographic, clinical, and radiological information available from the Asiri Central Brain Tumour Registry on 36 patients histologically diagnosed with low-grade gliomas that presented to the Asiri Central Hospital, Sri Lanka between 2019-2022 and 36 patients with high-grade gliomas. Initial bivariate analysis was conducted using chi-square tests and Fisher’s exact tests, followed by multivariate analysis using logistic regression to develop the final model.
Results: The mean age of the participants was 39.51 years (SD = 20.57 years), and the majority were males (n = 43, 59.37%). The initial bivariate analysis revealed that increased age (p = 0.004), hypertension (p = 0.005), dyslipidaemia(p = 0.042), total duration from symptom onset to presentation(p = 0.006) and contrast enhancement with IV gadolinium in MRI(p = 0.002) were significant factors for high-grade gliomas. The final logistic regression model identified male sex (p = 0.110, OR = 2.645), advanced age above 50 years(p = 0.014, OR = 5.217), speech deficit as a presenting complaint(p = 0.066, OR = 9.939) and contrast enhancement on MRI(p = 0.064, OR = 2.798) as predictors(Omnibus ꭓ2 (4) = 20.395, p < 0.001, R2 = 0.247 (Cox & Snell), 0.329 (Negelkerke) as predictors for high-grade gliomas.
Conclusion: Sociodemographic and clinical characteristics combined with features of conventional MRIs can be utilized by clinicians to predict glioma grade preoperatively to plan and prepare treatment strategies.