Maddalena Strumia1,2,3, Wilfried Reichardt1,2,3, Ori Staszewski4, Dieter Henrik Heiland5, Astrid Weyerbrock5, Irina Mader6, Michael Bock7. 1. German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, Heidelberg, Germany. 2. University Medical Center Freiburg, Radiology-Medical Physics, Breisacher Str. 60a, 79106, Freiburg, Germany. 3. German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, Germany. 4. University Medical Center Freiburg, Neuropathology, Breisacher Str. 64, Freiburg, Germany. 5. University Medical Center Freiburg, Neurosurgery, Breisacher Str. 64, Freiburg, Germany. 6. University Medical Center Freiburg, Neuroradiology, Breisacher Str. 64, Freiburg, Germany. 7. University Medical Center Freiburg, Radiology-Medical Physics, Breisacher Str. 60a, 79106, Freiburg, Germany. michael.bock@uniklinik-freiburg.de.
Abstract
OBJECTIVES: To differentiate between abnormal tumor vessels and regular brain vasculature using new quantitative measures in time-of-flight (TOF) MR angiography (MRA) data. MATERIALS AND METHODS: In this work time-of-flight (TOF) MR angiography data are acquired in 11 glioma patients to quantify vessel abnormality. Brain vessels are first segmented with a new algorithm, efficient monte-carlo image-analysis for the location of vascular entity (EMILOVE), and are then characterized in three brain regions: tumor, normal-appearing contralateral brain, and the total brain volume without the tumor. For characterization local vessel orientation angles and the dot product between local orientation vectors are calculated and averaged in the 3 regions. Additionally, correlation with histological and genetic markers is performed. RESULTS: Both the local vessel orientation angles and the dot product show a statistically significant difference (p < 0.005) between tumor vessels and normal brain vasculature. Furthermore, the connection to both histology and the gene expression of the tumor can be found-here, the measures were compared to the proliferation marker Ki-67 [MIB] and genome-wide expression analysis. The results in a subgroup indicate that the dot product measure may be correlated with activated genetic pathways. CONCLUSION: It is possible to define a measure of vessel abnormality based on local vessel orientation angles which can differentiate between normal brain vasculature and glioblastoma vessels.
OBJECTIVES: To differentiate between abnormal tumor vessels and regular brain vasculature using new quantitative measures in time-of-flight (TOF) MR angiography (MRA) data. MATERIALS AND METHODS: In this work time-of-flight (TOF) MR angiography data are acquired in 11 gliomapatients to quantify vessel abnormality. Brain vessels are first segmented with a new algorithm, efficient monte-carlo image-analysis for the location of vascular entity (EMILOVE), and are then characterized in three brain regions: tumor, normal-appearing contralateral brain, and the total brain volume without the tumor. For characterization local vessel orientation angles and the dot product between local orientation vectors are calculated and averaged in the 3 regions. Additionally, correlation with histological and genetic markers is performed. RESULTS: Both the local vessel orientation angles and the dot product show a statistically significant difference (p < 0.005) between tumor vessels and normal brain vasculature. Furthermore, the connection to both histology and the gene expression of the tumor can be found-here, the measures were compared to the proliferation marker Ki-67 [MIB] and genome-wide expression analysis. The results in a subgroup indicate that the dot product measure may be correlated with activated genetic pathways. CONCLUSION: It is possible to define a measure of vessel abnormality based on local vessel orientation angles which can differentiate between normal brain vasculature and glioblastoma vessels.
Entities:
Keywords:
Abnormality quantification; Glioblastoma; Time-of-flight magnetic resonance angiography; Vessels analysis
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