  Quantification of local metabolic tumor volume (MTV) chan-ges after
Chemo-radiotherapy would allow accurate tumor response evaluation. Currently,
local MTV changes in esophageal (soft-tissue) cancer are measured by
registering follow-up PET to baseline PET using the same transformation
obtained by deformable registration of follow-up CT to baseline CT. Such
approach is suboptimal because PET and CT capture fundamentally different
properties (metabolic vs. anatomy) of a tumor. In this work we combined PET and
CT images into a single blended PET-CT image and registered follow-up blended
PET-CT image to baseline blended PET-CT image. B-spline regularized
diffeomorphic registration was used to characterize the large MTV shrinkage.
Jacobian of the resulting transformation was computed to measure the local MTV
changes. Radiomic features (intensity and texture) were then extracted from the
Jacobian map to predict pathologic tumor response. Local MTV changes calculated
using blended PET-CT registration achieved the highest correlation with ground
truth segmentation (R=0.88) compared to PET-PET (R=0.80) and CT-CT (R=0.67)
registrations. Moreover, using blended PET-CT registration, the multivariate
prediction model achieved the highest accuracy with only one Jacobian
co-occurrence texture feature (accuracy=82.3%). This novel framework can
replace the conventional approach that applies CT-CT transformation to the PET
data for longitudinal evaluation of tumor response.
