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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1-SBIB-S (50))
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Zhang, Huiming
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University of California San Francisco
Schools of Medicine
San Francisco
United States
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