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Quantification of local metabolic tumor volume (MTV) chan-ges afterChemo-radiotherapy would allow accurate tumor response evaluation. Currently,local MTV changes in esophageal (soft-tissue) cancer are measured byregistering follow-up PET to baseline PET using the same transformationobtained by deformable registration of follow-up CT to baseline CT. Suchapproach is suboptimal because PET and CT capture fundamentally differentproperties (metabolic vs. anatomy) of a tumor. In this work we combined PET andCT images into a single blended PET-CT image and registered follow-up blendedPET-CT image to baseline blended PET-CT image. B-spline regularizeddiffeomorphic registration was used to characterize the large MTV shrinkage.Jacobian of the resulting transformation was computed to measure the local MTVchanges. Radiomic features (intensity and texture) were then extracted from theJacobian map to predict pathologic tumor response. Local MTV changes calculatedusing blended PET-CT registration achieved the highest correlation with groundtruth 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 multivariateprediction model achieved the highest accuracy with only one Jacobianco-occurrence texture feature (accuracy=82.3%). This novel framework canreplace the conventional approach that applies CT-CT transformation to the PETdata for longitudinal evaluation of tumor response.

Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response