This project is an academic industrial partnership (AIP) between researchers at UCSF and Sentinelle Medical, Inc., to implement a breast MR image analysis system for assessing response to treatment, on an existing image review workstation. The goal of this project is to better enable the integration of imaging biomarkers into clinical trials by making imaging biomarkers available in real-time as part of the clinical workflow for breast MRI. Investigators at UCSF lead a large multi-center effort investigating MRI and molecular markers for assessing breast cancer response to pre-operative chemotherapy. These efforts are expected to lead to subsequent trials with adaptive designs that incorporate quantitative MRI measurements to assess response and revise treatment. Sentinelle Medical, Inc., manufactures an imaging and biopsy system with advanced capabilities for breast MR imaging and intervention. The system includes a workstation and software platform, Aegis, for image review and biopsy guidance. Aegis is a DICOM-compliant software system that communicates with both the MRI scanner and PACS server. We propose in this AIP project to develop two new software modules that will be added to the Aegis platform to support real-time image analysis and display of biomarker data derived from dynamic contrast-enhanced (DCE) and other functional MRI data. The goal of Specific Aim 1 is to develop a therapeutic response module (TRM) that will provide capabilities for importing and analyzing images, and producing colorized displays and numeric biomarker information. Output from the TRM will be immediately available at the time of the exam to direct biopsy-targeting and for clinical assessment of treatment response. The goal of Specific Aim 2 is to develop a biomarker optimization module (BOM), designed to be a research tool for retrospective optimization, testing and comparison of imaging biomarkers based on clinical trial databases annotated with relevant clinical correlates and outcomes. The BOM will allow biomarker performance, for example, ability to predict disease-free survival, to be optimized by adjusting biomarker quantification parameters. The BOM will also allow alternative metrics to be compared, for example, area under the curve (AUC) and the pharmacokinetic parameter ktrans, both used to quantify contrast uptake kinetics. The BOM will support the training of imaging metrics based on accumulated clinical trials data and is expected to lead to refinements and improvements in the parametric analyses provided in the TRM. It is the intent of this AIP to provide Aegis workstations with TRM and BOM capabilities to the participating clinical trial sites. ? ?

Public Health Relevance

? Non-invasive imaging methods have the potential to accelerate the evaluation of new cancer treatments. Molecular and functional imaging techniques can provide sensitive measures of treatment effects and can serve as in vivo biomarkers of response to treatment. The goal of this academic-industrial partnership is to advance breast MRI technology to allow imaging biomarkers to be measured in the context of clinical trials for real-time assessment of response and determination of treatment modifications. This project will also support the optimization and comparison of MRI-based imaging biomarkers based on clinical outcomes. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA132870-01
Application #
7437229
Study Section
Special Emphasis Panel (ZRG1-SBIB-S (50))
Program Officer
Croft, Barbara
Project Start
2008-04-25
Project End
2013-01-31
Budget Start
2008-04-25
Budget End
2009-01-31
Support Year
1
Fiscal Year
2008
Total Cost
$482,956
Indirect Cost
Name
University of California San Francisco
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
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
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
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

Showing the most recent 10 out of 13 publications