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Gliomas are the most common type of primary brain tumors. Although gliomasare relatively rare, they are among the deadliest types of cancer, with asurvival rate of less than 2 years after diagnosis. Gliomas are challenging todiagnose, hard to treat and inherently resistant to conventional therapy. Yearsof extensive research to improve diagnosis and treatment of gliomas havedecreased mortality rates across the Global North, while chances of survivalamong individuals in low- and middle-income countries (LMICs) remain unchangedand are significantly worse in Sub-Saharan Africa (SSA) populations. Long-termsurvival with glioma is associated with the identification of appropriatepathological features on brain MRI and confirmation by histopathology. Since2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluatedstate-of-the-art machine learning methods to detect, characterize, and classifygliomas. However, it is unclear if the state-of-the-art methods can be widelyimplemented in SSA given the extensive use of lower-quality MRI technology,which produces poor image contrast and resolution and more importantly, thepropensity for late presentation of disease at advanced stages as well as theunique characteristics of gliomas in SSA (i.e., suspected higher rates ofgliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a uniqueopportunity to include brain MRI glioma cases from SSA in global effortsthrough the BraTS Challenge to develop and evaluate computer-aided-diagnostic(CAD) methods for the detection and characterization of glioma inresource-limited settings, where the potential for CAD tools to transformhealthcare are more likely.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)