New York University School of Medicine

Diffusion MRI of Treatment Response for De-Escalation of Radiation Therapy

Grant Number: UG3CA228699

MRI has been used to map the internal treatment response of a tumor in addition to volume change. Among many MRI methods, diffusion MRI (dMRI) has been the modality of choice to assess the cellular microstructural properties of tumors. However, quantitative dMRI remains challenging as dMRI data represent different biophysical properties of tissue depending on diffusion weighting strength (q) and diffusion time (t) used for the measurement. The overarching goal of this study is to establish a quantitative methodology to utilize both q- and t-dependencies of dMRI data, as a tailored approach to quantify cell viability, cellular metabolism and perfusion from this non-contrast MRI method. Previous studies have demonstrated that both diffusion coefficient D and diffusional kurtosis coefficient K are promising imaging markers for cell viability. Cellular metabolism can be evaluated in terms of the water exchange time τex, measured by the diffusion time-dependent K, that is regulated by the ATP-dependent trans-membrane ion channels co-transporting water molecules as well as aquaporins, integral membrane proteins for passive water exchange. Intravoxel incoherent motion MRI metrics (pseudo diffusivity, D_p; perfusion fraction, f_p) can provide information about perfusion flow. In this study, we will further optimize and establish a set of quantitative non-contrast imaging markers of cell viability (D and K), cellular metabolism (τex), and perfusion (f_p⋅D_p) as a clinical tool for assessment of treatment response and validate it in clinical trials.

The goal for the UG3 phase is to optimize the proposed dMRI methods using numerical simulation, phantom test, and test-retest variability measurement. Additionally, the UH3 phase will focus on determining dMRI metrics most suitable for assessment of chemoradiation treatment response. Our central hypothesis is that the proposed combination of quantitative dMRI measures will better identify patients who have the potential to benefit from adaptive de-escalation or escalation of therapy. Recent studies showed that a subgroup of patients with human-papilloma virus-positive oropharyngeal squamous cell carcinoma have significantly better prognosis. These clinical data lead to important considerations to de-intensify treatment for this low-risk, younger population in order to reduce acute and chronic toxicity without compromising disease control. It has been suggested that the adaptive de-escalation of treatment can be tailored for individual patients based on the early tumor volume change. However, volumetric assessment is often inadequate because the treatment response of a tumor can be heterogeneous in terms of cell viability, cellular metabolism, and perfusion that are relevant to the success of any chemoradiation therapy. These complex internal changes in a tumor may not be adequately represented by tumor volume change at the early stage. The data acquisition and analysis software tools for q- and t-dependent dMRI data to be developed in this study will enable comprehensive and quantitative assessment of cancer treatment response to tailor chemoradiation therapies for individual patients.