  Clinical diagnostic and treatment decisions rely upon the integration of
patient-specific data with clinical reasoning. Cancer presents a unique context
that influence treatment decisions, given its diverse forms of disease
evolution. Biomedical imaging allows noninvasive assessment of disease based on
visual evaluations leading to better clinical outcome prediction and
therapeutic planning. Early methods of brain cancer characterization
predominantly relied upon statistical modeling of neuroimaging data. Driven by
the breakthroughs in computer vision, deep learning became the de facto
standard in the domain of medical imaging. Integrated statistical and deep
learning methods have recently emerged as a new direction in the automation of
the medical practice unifying multi-disciplinary knowledge in medicine,
statistics, and artificial intelligence. In this study, we critically review
major statistical and deep learning models and their applications in brain
imaging research with a focus on MRI-based brain tumor segmentation. The
results do highlight that model-driven classical statistics and data-driven
deep learning is a potent combination for developing automated systems in
clinical oncology.
