MRI Diffusion in Tumors using Oscillating Gradients

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Abstract / Summary This revised competitive renewal seeks to extend the technical developments of the previous funding period to validate and apply a novel diffusion-based MR imaging technique, quantitative temporal diffusion spectroscopy (qTDS), that provides unique information on tissue microstructure and in particular can reveal early changes in tumors after treatment. In the previous cycle we developed this innovative method and showed it is a sensitive indicator of changes in cell dimensions and tissue microstructure such as those that occur with cell division and during apoptosis, before frank changes occur in cell density or tumor volume. As such, qTDS has considerable potential for assessing whether specific treatment regimens are working, and so may inform the selection of optimal therapies for patients and the reduction of avoidable side-effects. QTDS is based on measurements of water diffusion rates over different time scales corresponding to different spatial dimensions. We have previously shown it can detect changes in intracellular structure and cell sizes and density, in cell cultures and in animal models, early in the course of a treatment and without some of the confounding factors that affect other diffusion techniques, such as changes in cell membrane permeability. We have performed theoretical analyses, computer simulations, and cell and in vivo animal studies, to understand the factors that affect qTDS measurements, and have implemented the first practical qTDS acquisitions on a human 3T scanner. In the current proposal we aim to extend our previous work and use qTDS as an in vivo imaging technique for non-invasive characterization of specific cellular changes which are currently assessable only via invasive biopsy. We propose to validate qTDS in cell and animal models of cancer, and determine whether qTDS is capable of detecting treatment-induced cell size changes early in specific therapeutic regimens. We also propose to translate qTDS clinically by demonstrating its performance in predicting neoadjuvant treatment response in breast cancer. We hypothesize that qTDS is capable of characterizing the distinct cellular changes associated with treatment-induced apoptosis, thereby providing an innovative and unique means of assessing tumor response at an early stage of therapy. Our specific aims are: [i] in a transgenic mouse model of breast cancer, we will quantitatively map tumor cell size and density in vivo, and validate the qTDS derived parameters on a voxel by voxel basis using using quantitative, co-registered histology: [ii] in mouse models of breast cancer treated by different targeted drugs, we will evaluate qTDS as an imaging biomarker capable of detecting treatment-induced apoptosis and predicting treatment efficacy early during therapy: [iii] In human breast cancer patients, we will evaluate qTDS as an imaging biomarker for assessing breast tumor early response to neoadjuvant chemotherapy and predicting treatment efficacy after the first and subsequent cycles of treatment by correlating imaging data with clinical and pathological responses. The proposed qTDS method has the potential to measure cell size changes in vivo and improve the assessment of treatment response and thereby contribute to personalized clinical cancer care.