Contrast enhancement on MRI (magnetic resonance imaging) provides the best currently available approach for measuring tumor response to therapy in brain and other solid tumors. Accordingly, criteria have been formulated, by international committees, to guide the assessment of tumor burden by measuring tumor diameters of contrast-enhancing tumor (eg RECIST, Macdonald and RANO criterion). Despite the widespread use of post-contrast MRI in daily practice and most clinical trials, there exist several important limitations to this approach for use in brain tumors. First, malignant gliomas are histopathologically and radiographically heterogenous in appearance with geographically irregular margins, variable enhancement, and regions of central necrotic or cystic changes, making the subjective and manual identification and measurement of enhancing ROIs extremely challenging. Second, assessment of postoperative tumor volume can be confounded by the presence of blood products, which also appear bright on post-contrast MRI. Though a visual comparison between pre and post-contrast T1-weighted images is often sufficient to make this distinction, this can be very challenging when the enhancement is subtle. Finally, with the increasing use of anti-angiogenic agents, which have a steroid-like effect, the number of cases with subtle post-contrast enhancement is becoming increasingly common. These challenges may explain the large inter-observer differences (greater than 50%) in assessing tumor burden that plague most clinical trials. To overcome these limitations, we propose to develop the delta T1 (dT1) method for automatic detection of true contrast-agent enhancement and the automatic generation of enhancing ROIs with options to generate RECIST/Macdonald/RANO metrics. These tools will be compared against standard approaches for the assessment of tumor burden as outlined in Aim 2. [The novelty of the dT1 method derives from the fact that it incorporates a patented "image intensity standardization" technology, giving the newly developed tools an important advantage over existing methods and the potential to shift clinical practice paradigms.] Specifically, the standardization step, which has been patented and exclusively licensed to Imaging Biometrics LLC, eliminates much of the normal variability in image contrast due to normal MRI system variability, slight differences in imaging parameters and the like. Thus, the dT1 and associated ROI tools, which will be incorporated into Imaging Biometrics low-cost product, IB SuiteTM, will be made available on a widespread basis. It will enable the robust and reproducible determination of tumor ROIs that eliminate or significantly minimize current issues of inter-observer variability. Accordingly, productizing this tool has the potential to result in a paradigm shift in how tumor burden is assessed in clinical trials and daily practice, improving reliability and workflow resulting in better care for patients with brain tumors.

Public Health Relevance

The goal of this Phase I STTR proposal is the development of much needed MR image analysis tools for the robust and automatic determination of brain tumor burden. The incorporation of novel standardization algorithms, creation of difference or "deltaT1" maps, as well as automatic ROI methods whose thresholds are dictated by biologic indicators, give the developed tools a high likelihood of significantly diminishing the high intra- and inter-observer differences that plague current methods. The development and validation of these tools will be performed in collaboration with Imaging Biometrics LLC, a small business concern, who has a proven track record of translating promising laboratory medical image analysis software into clinical tools. Therefore the developed tools will be made widely available for the daily clinical assessment of tumor response to therapy, as well as for larger scale clinical trials. This in turn can result in the performance of more efficient and cost-effective clinical trials, as well as improved care of patients on an individualized basis.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Small Business Technology Transfer (STTR) Grants - Phase I (R41)
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Special Emphasis Panel (ZRG1-ETTN-K (10))
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Fertig, Stephanie
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Imaging Biometrics, LLC
Elm Grove
United States
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