Neo-adjuvant chemotherapy with temozolomide, along with radiation therapy has become the standard of care for the treatment of gliomas as this regimen improves survival of newly diagnosed glioblastoma patients. However, in about a third of the patients, progressive and enhancing lesions on MRI occur within six months after end of chemo-radiation therapy, which resolve without further interventions. These lesions are termed as pseudo-progression (PsP) as they mimic the appearance of a tumor on standard clinical MRI. Patients with PsP have a longer survival indicating that the progressive lesions were due to treatment effect rather than due to true tumor progression (TP). Accurate identification of PsP is critical for patient management as PsP patients can avoid unnecessary repeat surgery and continue effective chemotherapy, while alternative treatments, including resection or immunotherapy can be offered for TP. Functional MRI tools, such as, diffusion tensor imaging (DTI), perfusion imaging and magnetic resonance spectroscopy (MRS), probe changes in water mobility, blood flow and tissue metabolism, which are more sensitive in detecting changes than anatomical changes observed by standard MRI. Furthermore, in a clinical setting, it is unlikely that a single imaging parameter or modality will suffice in accurae detection of PsP. We thus hypothesize that the combination of whole brain MRS, perfusion imaging and DTI can provide a more accurate detection of PsP. In order to test this hypothesis, we will develop a novel decision support system that uses important features from all the imaging modalities in a completely non-biased way. Specifically, we will achieve the following aims: SA1: To assess the utility of 3D EPSI in differentiation of PsP patients from TP. SA2: To determine the role of dynamic susceptibility weighted contrast imaging in differentiation of PsP from TP. SA3: To evaluate the accuracy of DTI parameters, such as mean diffusivity (MD), fractional anisotropy (FA) and tensor shape measures including linear anisotropy coefficient (CL), planar anisotropy coefficient (CP) and spherical anisotropy coefficient (CS) in the identification of PsP. SA4: To determine the best combination of imaging parameters for accurate diagnosis of PsP. The utility of these parameters will be tested both at first clinical presentation (baseline) as well as change in these parameters on one-month follow up scan. A Bayesian network based decision support system will be used to detect PsP which will include advanced imaging parameters, as well as change in these parameters from baseline. The novelty of this approach is that it will provide a multi-dimensional analysis of brain tumors, whic will facilitate an accurate diagnosis of PsP and avoid unnecessary second-look surgery in these patients.

Public Health Relevance

This project is directed towards an accurate diagnosis of pseudo-progression using novel and advanced MRI and MRS methods. Glioblastomas account for a majority of primary brain tumors and are often treated by surgery, followed by temozolomide and radiation therapy. In almost a third of the patients treated with temozolomide plus radiation therapy, progressively increasing mass lesion, mimicking a tumor, is observed soon after completion of chemo-radiation therapy. It is critical to accurately identify these pseudo tumors as the management of pseudo-progression is completely different than that of a tumor. Standard imaging methods are not useful to diagnose pseudo-progression and hence we are proposing to use advanced imaging methods for its diagnosis. We will implement a fast novel high resolution 3D MR spectroscopic method along with diffusion tensor imaging and perfusion imaging to accurately detect pseudo-progression. A decision support system using Bayesian analysis will be developed that will include the findings of these imaging methods as well as a change in parameter values. Development of such a decision-support system using imaging based markers would allow earlier triage of pseudo-progression for conservative treatment, while patients with true progression can be treated with alternative strategies much earlier.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZRG1-SBIB-W (56))
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Henderson, Lori A
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University of Pennsylvania
Schools of Medicine
United States
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Wang, S; Martinez-Lage, M; Sakai, Y et al. (2016) Differentiating Tumor Progression from Pseudoprogression in Patients with Glioblastomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI. AJNR Am J Neuroradiol 37:28-36
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