  Gliomas are the most common type of primary brain tumors. Although gliomas
are relatively rare, they are among the deadliest types of cancer, with a
survival rate of less than 2 years after diagnosis. Gliomas are challenging to
diagnose, hard to treat and inherently resistant to conventional therapy. Years
of extensive research to improve diagnosis and treatment of gliomas have
decreased mortality rates across the Global North, while chances of survival
among individuals in low- and middle-income countries (LMICs) remain unchanged
and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term
survival with glioma is associated with the identification of appropriate
pathological features on brain MRI and confirmation by histopathology. Since
2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated
state-of-the-art machine learning methods to detect, characterize, and classify
gliomas. However, it is unclear if the state-of-the-art methods can be widely
implemented in SSA given the extensive use of lower-quality MRI technology,
which produces poor image contrast and resolution and more importantly, the
propensity for late presentation of disease at advanced stages as well as the
unique characteristics of gliomas in SSA (i.e., suspected higher rates of
gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique
opportunity to include brain MRI glioma cases from SSA in global efforts
through the BraTS Challenge to develop and evaluate computer-aided-diagnostic
(CAD) methods for the detection and characterization of glioma in
resource-limited settings, where the potential for CAD tools to transform
healthcare are more likely.
