Prediction of therapy on cancer
The impact of therapy on cancer as well as normal organs is hard to predict by conventional means, including tumor size, mean value of physiological image parameters in the tumor, or even voxel-by-voxel analysis of physiological images taken before and during treatment. A change in tumor size often occurs too late for prediction of response. A mean value of a physiological image parameter in the tumor, the distribution of which is often heterogeneous, loses sensitivity. Voxel-by-voxel analysis of physiological image changes in the tumor during an observation period often fails at the location where the tumor volume grows or shrinks during the observation interval due to image mis-registration.
Aggressive sub-volume identification
A research group in the Radiology and Medical School of University of Michigan has reported a technology that finds aggressive sub-volume of tumors to predict for local treatment outcome of therapies on cancers in the early course of treatment. Such results can allow for treatment to be terminated early if ineffective. The technology uses physiological imaging data and probabilistic classification and clustering method to calculate sub-volumes of volume-weighted physiological parameters, and has been testified on a group of brain cancer patients. As a result, the technology is far more robust than existing voxel-level physiological analyses. Also, it improves the robustness of analysis for imaging of patients with various underlying physiology and imaged on MRI and other scanners.
- Early prediction of systemic as well as focal cancer therapies.
- More robust and reliable than existing voxel-level physiological analyses
- Improved robustness of analysis with various underlying physiology and scanners.