  Glioblastomas are the most common malignant brain tumors in adults.
Approximately 200000 people die each year from Glioblastoma in the world.
Glioblastoma patients have a median survival of 12 months with optimal therapy
and about 4 months without treatment. Glioblastomas appear as heterogeneous
necrotic masses with irregular peripheral enhancement, surrounded by vasogenic
edema. The current standard of care includes surgical resection, radiotherapy
and chemotherapy, which require accurate segmentation of brain tumor
subregions. For effective treatment planning, it is vital to identify the
methylation status of the promoter of Methylguanine Methyltransferase (MGMT), a
positive prognostic factor for chemotherapy. However, current methods for brain
tumor segmentation are tedious, subjective and not scalable, and current
techniques to determine the methylation status of MGMT promoter involve
surgically invasive procedures, which are expensive and time consuming. Hence
there is a pressing need to develop automated tools to segment brain tumors and
non-invasive methods to predict methylation status of MGMT promoter, to
facilitate better treatment planning and improve survival rate. I created an
integrated diagnostics solution powered by Artificial Intelligence to
automatically segment brain tumor subregions and predict MGMT promoter
methylation status, using brain MRI scans. My AI solution is proven on large
datasets with performance exceeding current standards and field tested with
data from teaching files of local neuroradiologists. With my solution,
physicians can submit brain MRI images, and get segmentation and methylation
predictions in minutes, and guide brain tumor patients with effective treatment
planning and ultimately improve survival time.
