The goal of this project is to develop and optimize MR imaging biomarkers for the assessment of breast cancer response to pre-operative treatment. The project is a continuation of effort under Academic-Industrial Partnership (AIP) R01 grant award CA 13270 entitled Real-time in vivo MRI biomarkers for breast cancer pre-operative trials. In the prior funding cycle researchers at UCSF and Hologic (formerly Sentinelle Medical) developed and deployed dedicated workstations to perform volumetric analysis of breast MR images in the multi-center I- SPY TRIAL (Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And molecular analysis) clinical trial. We successfully migrated the signal enhancement ratio (SER) tumor volume measurement method, developed at UCSF and used in the American College of Radiology Imaging Network (ACRIN) trial 6657, to the Hologic Aegis software platform. The Aegis SER software plug-in received IDE approval in 2010 for use in the I-SPY 2 TRIAL and Aegis systems were installed at the 20 I-SPY 2 clinical centers. Tumor volume measurements generated from the Aegis SER plug-in software are now incorporated in the adaptive phase II trial design for I-SPY 2 and used in the patient randomization algorithm. Under a second aim in the original project period, we developed automated image analysis tools to explore how quantification parameters influence the effectiveness of imaging metrics for predicting clinical outcomes. The continuing project focuses on expanding Aegis software functionality to include real-time analysis of diffusion-weighted MRI (DWI) for assessing response, and prospectively testing imaging metrics that have been optimized using the automated tools developed in the previous project period. The work proposed will be coordinated with the I-SPY 2 trial and its imaging sub-study ACRIN 6698, testing breast DWI for assessment of tumor response.
Under Specific Aim 1 we will add DWI image analysis and reporting capabilities to the Aegis workstation for measuring breast tumor apparent diffusion coefficient (ADC).
Under Specific Aim 2 we will prospectively test the predictive performance of breast cancer subtype-optimized FTV metrics estimated by the retrospective analysis of I-SPY 1 data. The prospective studies will be performed using data from an independent cohort of patients enrolled in the control arm of I-SPY 2. Exploratory studies will also be performed to investigate and compare alternative metrics using DCE and DWI image data from I-SPY 2.
Realization of quantitative imaging (QI) biomarkers requires enabling technologies to perform QI measurement in the clinical trials time frame and environment. This project aims to develop an integrated software platform to measure response by MRI for patients undergoing preoperative chemotherapy for breast cancer.
|Partridge, Savannah C; Zhang, Zheng; Newitt, David C et al. (2018) Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial. Radiology 289:618-627|
|Hylton, Nola M (2018) Residual Disease after Neoadjuvant Therapy for Breast Cancer: Can MRI Help? Radiology 289:335-336|
|Olshen, Adam; Wolf, Denise; Jones, Ella F et al. (2018) Features of MRI stromal enhancement with neoadjuvant chemotherapy: a subgroup analysis of the ACRIN 6657/I-SPY TRIAL. J Med Imaging (Bellingham) 5:011014|
|Bane, Octavia; Hectors, Stefanie J; Wagner, Mathilde et al. (2018) Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study. Magn Reson Med 79:2564-2575|
|Newitt, David C; Malyarenko, Dariya; Chenevert, Thomas L et al. (2018) Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 5:011003|
|Wolf, Denise M; Yau, Christina; Sanil, Ashish et al. (2017) DNA repair deficiency biomarkers and the 70-gene ultra-high risk signature as predictors of veliparib/carboplatin response in the I-SPY 2 breast cancer trial. NPJ Breast Cancer 3:31|
|Li, Wen; Arasu, Vignesh; Newitt, David C et al. (2016) Effect of MR Imaging Contrast Thresholds on Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes: A Subgroup Analysis of the ACRIN 6657/I-SPY 1 TRIAL. Tomography 2:378-387|
|Wilmes, Lisa J; Li, Wen; Shin, Hee Jung et al. (2016) Diffusion Tensor Imaging for Assessment of Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer. Tomography 2:438-447|
|Lo, Wei-Ching; Li, Wen; Jones, Ella F et al. (2016) Effect of Imaging Parameter Thresholds on MRI Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes. PLoS One 11:e0142047|
|Malyarenko, Dariya I; Newitt, David; J Wilmes, Lisa et al. (2016) Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials. Magn Reson Med 75:1312-23|
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