Monday, May 29, 2023
5/29/2023
Project Summary Quantitative imaging of tumor biological functions have been shown superior to imaging tumor size for prediction and evaluation of cancer response to therapy. Conventionally used as a noninvasive imaging method to assess microvascular perfusion and permeability, dynamic contrast-enhanced (DCE) MRI is increasingly employed in research and early phase clinical trial settings to measure and, importantly, predict tumor response to treatment. The standard two- or three-parameter Tofts models (TMs) are the most commonly used for pharmacokinetic (PK) modeling of DCE-MRI data to estimate quantitative imaging biomarkers such as Ktrans and ve. However, the TM is suboptimal in that it ignores the real physiological phenomenon of water exchange between tissue compartments when quantifying tissue concentration of contrast agent from MRI signal intensities. The Shutter-Speed Model (SSM) developed by the Oregon Health & Science University (OHSU) group is a more comprehensive PK model, taking into account the intercompartmental water exchange kinetics. Recent single-center OHSU studies have demonstrated superior ability of SSM DCE-MRI for prediction and evaluation of therapy response in breast cancer compared to the TM. Furthermore, it was recently discovered that the SSM-exclusive parameter, ti (mean intracellular water lifetime), is a new imaging biomarker of metabolic activity, and was the only baseline (pre-treatment) marker predictive of response to neoadjuvant chemotherapy (NAC) in breast cancer and overall survival in head and neck cancer. ti also has the advantage of being significantly less sensitive to variation in arterial input function (AIF) than the conventional PK parameters. Using the data acquisition and analysis protocols optimized by the OHSU group, the overall goal of this project is to validate the robustness of SSM DCE-MRI as a quantitative imaging tool for assessment of cancer therapy response in a prospective study under a multicenter setting across three major MRI scanner platforms, using NAC treatment of breast cancer as the testing clinical application. Specifically, we will (1) implement the optimized SSM DCE-MRI data acquisition and analysis protocols and perform QA/QC in a multicenter setting; (2) conduct the multicenter prospective study to validate the utility of SSM DCE-MRI for prediction and evaluation of breast cancer response to NAC; and (3) refine an OHSU-developed web-based clinical decision support system by developing and incorporating a predictive model of therapy response that integrates imaging markers with clinical and histopathological data, and evaluate the system adaptability in clinical workflow.
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