This EAGER project "Restriction Spectrum Imaging for evaluating glioma treatment response" will create a unique data set that can be used by theoretical scientists to understand how these hard to treat tumors respond and evade treatment. This high-precision data is needed for scientists to be able to distinguish between different models of cancer growth that currently exist. Understanding tumor growth during treatment will help with the regiment of medicines used for glioma treatment and will reveal how some cells die and others evade current therapies, which is essential for the development of better strategies to treat this lethal disease. The proposed technique is based on the different physics of normal and cancerous tissue and fits well within the Physics of Living Systems (PoLS) program at NSF as a high-risk, but high-return project.
The need for a human tumor growth data set that is well characterized and with low noise, which can be used by the theoretical physics community to test novel theories of tumor kinetics was recognized at: "The Physical Principles of Human Cancer Imaging Workshop" held on November 4-5, 2013 in Boston. This bold project, from a young scientist, reflects this need and the objective of this research is to evaluate the utility of a novel, non-invasive Magnetic Resonance Imaging technique, Restriction Spectrum Imaging (RSI), to detect and quantify tumor response to treatment in glioma patients. The specific objectives are to 1) procure and organize existing (and ongoing) MRI data sets (including RSI) collected across time on glioma patients at UCSD Moores Cancer Center who receive standard of care therapy which may include radiation, chemo, and anti-VEGF treatments. 2) With the help of a board certified neuro-radiologist, the second objective would be to evaluate glioma treatment response in these patients using standard published radiographic response criteria (RANO/McDonald/RECIST). 3) The final objective would be to analyze and quantify tumor cell populations directly using RSI (including the spatial extent (3D volume) and density (intensity-weighted volume)) and compare these quantitative RSI measures with the standard response criteria. RSI is a technique developed by the PI that has recently been shown to be resistant to pseudo-response and pseudo-progression, both of which plague standard radiographic response criteria and pose severe limitations to researchers involved in mathematical and physical modeling of tumor spread and resistance. RSI quantitative measures of tumor cell populations that are insensitive to pseudo-response and pseudo-progression will allow researchers and clinicians to further understand the temporal and treatment dynamics of cancer cells, to develop more targeted cancer treatments and to advance mathematical and physical models of the disease. RSI mapping of tumor cell populations may ultimately transform clinical practice and change clinical care for cancer patients. By providing an objective and quantitative measure of tumor cell volume and density directly on imaging non-invasively, patients who undergo cancer treatment may be evaluated and monitored over time with RSI to see if and where the cancer spreads to or alternatively if the particular cancer treatment is working.
