By combining the strengths of various imaging modalities, the multimodality imaging approach has potential to improve
tumor staging, delineation of tumor boundaries, chemo-radiotherapy regime design, and treatment response assessment
in cancer management. To address the urgent needs for efficient tools to analyze large-scale clinical trial data, we have
developed an integrated multimodality, functional and anatomical imaging analysis software package for target
definition and therapy response assessment in pediatric radiotherapy (RT) patients. Our software provides quantitative
tools for automated image segmentation, region-of-interest (ROI) histogram analysis, spatial volume-of-interest (VOI)
analysis, and voxel-wise correlation across modalities. To demonstrate the clinical applicability of this software,
histogram analyses were performed on baseline and follow-up 18F-fluorodeoxyglucose (18F-FDG) PET images of nine
patients with rhabdomyosarcoma enrolled in an institutional clinical trial at St. Jude Children's Research Hospital. In
addition, we combined 18F-FDG PET, dynamic-contrast-enhanced (DCE) MR, and anatomical MR data to visualize the
heterogeneity in tumor pathophysiology with the ultimate goal of adaptive targeting of regions with high tumor burden.
Our software is able to simultaneously analyze multimodality images across multiple time points, which could greatly
speed up the analysis of large-scale clinical trial data and validation of potential imaging biomarkers.