TY - JOUR
T1 - Tracking the Adaptation and Compensation Processes of Patients’ Brain Arterial Network to an Evolving Glioblastoma
AU - Zhu, Junxi
AU - Teolis, Spencer
AU - Biassou, Nadia
AU - Tabb, Amy
AU - Jabin, Pierre Emmanuel
AU - Lavi, Orit
N1 - Funding Information:
OL would like to thank Dr. Michael M. Gottesman (Laboratory of Cell Biology, CCR, NCI, NIH) and Dr. Tom Misteli (Laboratory of Receptor Biology and Gene Expression, CCR, NCI, NIH) for their critical comments and support. The authors would also like to thank Mr. George Leiman (LCB, NCI, NIH) for editorial assistance, and Andrea Beri from NIH/CC/BTRIS program for providing the data. This work was supported by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research. All authors wrote, read and approved the manuscript.
Publisher Copyright:
© 2022 IEEE Computer Society. All rights reserved.
PY - 2022/1
Y1 - 2022/1
N2 - 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 over time. Our personalized approach provides a valuable analysis tool that could augment current clinical assessments of the progression of glioblastoma and other neurological disorders affecting the brain.
AB - 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 over time. Our personalized approach provides a valuable analysis tool that could augment current clinical assessments of the progression of glioblastoma and other neurological disorders affecting the brain.
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U2 - 10.1109/TPAMI.2020.3008379
DO - 10.1109/TPAMI.2020.3008379
M3 - Article
C2 - 32750811
AN - SCOPUS:85098851441
SN - 0162-8828
VL - 44
SP - 488
EP - 501
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 1
ER -