Tracking the adaptation and compensation processes of patients' brain arterial network to an evolving glioblastoma

Authors: ZHU, JUNXI - National Institutes Of Health (NIH); TEOLIS, SPENCER - National Institutes Of Health (NIH); BIASSOU, NADIA - National Institutes Of Health (NIH); Tabb, Amy; JABIN, PIERRE-EMMANUEL - University Of Maryland; LAVI, ORIT - National Institutes Of Health (NIH)

Submitted to: Institute of Electrical and Electronics Engineers (IEEE) on Pattern Analysis and Machine Intelligence (TPAMI)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/1/2020
Publication Date: 7/9/2020
Citation: Zhu, J., Teolis, S., Biassou, N., Tabb, A., Jabin, P., Lavi, O. 2020. Tracking the adaptation and compensation processes of patients' brain arterial network to an evolving glioblastoma. Institute of Electrical and Electronics Engineers (IEEE) on Pattern Analysis and Machine Intelligence (TPAMI). https://doi.org/10.1109/TPAMI.2020.3008379.
DOI: https://doi.org/10.1109/TPAMI.2020.3008379

Interpretive Summary: A brain cancer called glioblastoma (GBM) affects the flow of blood to parts of the brain. We developed a method to infer which changes in a patient’s blood flow are due to the interplay between the development of the tumor and compensation processes of the brain. Our automated method could be used in treatment of patients to identify markers of GBM or other neurological disorders affecting the brain.

Technical Abstract: The brain’s vascular network dynamically affects its development and core functions. It rapidly responds to abnormal conditions by adjusting properties of the network, aiding stabilization and regulation of brain activities. Tracking prominent arterial changes has clear clinical and surgical advantages. However, the arterial network functions as a system; thus, local changes may imply global compensatory effects that could impact the dynamic progression of a disease. We developed automated personalized system-level analysis methods of the compensatory arterial changes and mean blood flow behavior from a patient’s clinical images. By applying our approach to data from a patient with aggressive brain cancer compared with healthy individuals, we found unique spatiotemporal patterns of the arterial network that could assist in predicting the evolution of glioblastoma (GBM) over time. Our personalized approach provides a valuable analysis tool that could augment current clinical assessments of the progression of GBM and other neurological disorders affecting the brain.