Stepwise heterogeneity analysis of breast tumors in perfusion DCE-MRI datasets
2012
The signal curves in perfusion dynamic contrast enhanced MRI (DCE-MRI) of cancerous breast tissue reveal valuable
information about tumor angiogenesis. Pathological studies have illustrated that breast tumors consist of different subregions,
especially with more homogeneous properties during their growth. Differences should be identifiable in DCEMRI
signal curves if the characteristics of these sub-regions are related to the perfusion and angiogenesis. We introduce
a stepwise clustering method which in a first step uses a new similarity measure. The new similarity measure (PM)
compares how parallel washout phases of two curves are. To distinguish the starting point of the washout phase, a linear
regression method is partially fitted to the curves. In the next step, the minimum signal value of the washout phase is
normalized to zero. Finally, PM is calculated according to maximal variation among the point wise differences during
washout phases. In the second step of clustering the groups of signal curves with parallel washout are clustered using
Euclidean distance. The introduced method is evaluated on 15 DCE-MRI breast datasets with different types of breast
tumors. The use of our new heterogeneity analysis is feasible in single patient examination and improves breast MR
diagnostics.
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