In this multi-site breast cancer study, we use an MRI method called intravoxel incoherent motion (IVIM) to characterize tumor microenvironment and predict response to neoadjuvant chemotherapy.
Advances in the understanding of molecular subtypes of breast cancer have led to improved treatment and outcomes. However, even targeted therapies have mixed results because of the heterogeneity of intratumor microenvironment, underlining how important full characterization of the heterogeneity and complexity of tumors is for the development of effective, personalized imaging biomarkers. Recent research has shown that reducing the variability of quantitative imaging biomarkers can pay tremendous diagnostic and prognostic dividends. However, the development and validation of such biomarkers require crossing institutional, commercial, and analytical boundaries. We address this challenge in the BRIMM project.
The BRIMM project is a multi-site breast cancer study aimed at developing a robust, translation-ready technique for predicting response to neoadjuvant chemotherapy by targeting the chemotherapy setting with a quantitative MRI method called intravoxel incoherent motion (IVIM). This approach probes two key features of the tumor microenvironment—angiogenesis and cellularity—noninvasively. A successful method has the potential to allow non-responding breast cancer patients to change treatment, leading to lower toxicity and better response.
The project proceeds in two phases. First, we assess IVIM measurement variability across five collaborating sites and maximize cross-platform accuracy for clinical implementation of IVIM in breast cancer patients. This includes the use of retrospective clinical data, digital-phantom data and a manufacturable breast flow phantoms. Second, we conduct prospective studies at two sites, focusing on the IVIM parameters shown to be robust predictors of response. In this phase, we collect IVIM from enrolled patients prior to and early in their treatment, and analyze it in combination with all other acquired data to generate a best practices algorithm for application in future breast cancer neoadjuvant therapy trials.
Figure 1. (a-e): Flow phantom, syringe pump, and DWI slices in (g,h). (f) Transition temperatures of liquid crystal (LC) elements. (g, h): ADC maps in PVP vials (g) and sponge compartments (h), showing elevated ADC with flow. (i) T1-weighted MRI of LC elements. (j) IVIM signal decays vs. flow rate in mL/min.
Figure 2. Maps of tissue diffusivity Dt, perfusion fraction fp, and pseudodiffusivity Dp in a breast cancer patient from 3 software packages.
Project Lead
We acknowledge support from the following NIH grants: UG3CA239861, UH3CA239861, P41 EB017183, P30CA016087, P30CA008748, R01CA207290, R01CA248192, R01CA190299.
Researchers at the Center for Biomedical Imaging at NYU Langone Health develop transformative imaging technologies to advance basic science and address unsolved clinical problems.
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