Assessment of anti-angiogenic therapies for the most severe form of brain cancer, glioblastoma, is extremely timely given the recent approval of bevacizumab yet the moderate response rate and the challenging side effects of these therapies. Clinical decision-making tools are badly needed;fortunately, our recently published data suggest that measurement of microvascular properties of the tumor using MRI and gadolinium-based approaches could be very useful, as with proper quantitation these methods appear to be capable of serving as an effective prognostic imaging biomarker, and may be beneficially combined with blood biomarkers. We propose to join the NCI's Quantitative Imaging Network (QIN) and develop improved analysis methods for dynamic contrast enhanced MRI and dynamic susceptibility MRI that will improve quantification and decrease variability. We propose to develop techniques that will be applicable in the multicenter setting through a bottom-up approach of simulations, phantom studies, retrospective analysis, and prospective analysis in patients undergoing treatment with anti-angiogenic therapies. We anticipate that our proposed approach, in particular through working in close harmony with the QIN, will improve the reliability of advanced microvascular MRI methods as potential imaging biomarkers, and pave the way for a clinically useful decision-making tool.
Advanced MRI methods may improve our ability to provide an accurate prognosis and potentially guide treatment choices for glioblastoma patients. Our proposed research will help establish a common, standardized approach to acquisition and analysis of two forms of vascular MRI that have shown excellent promise. We will do this by careful reduction of variability and by close participation in the National Cancer Institute's Quantitative Imaging Network. These efforts will enable these advanced techniques to become more widely available and more appropriately establish their benefit to patients.
|Chang, Ken; Balachandar, Niranjan; Lam, Carson et al. (2018) Distributed deep learning networks among institutions for medical imaging. J Am Med Inform Assoc 25:945-954|
|Balagurunathan, Yoganand; Beers, Andrew; Kalpathy-Cramer, Jayashree et al. (2018) Semi-automated pulmonary nodule interval segmentation using the NLST data. Med Phys 45:1093-1107|
|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|
|Paul, Rahul; Hawkins, Samuel H; Schabath, Matthew B et al. (2018) Predicting malignant nodules by fusing deep features with classical radiomics features. J Med Imaging (Bellingham) 5:011021|
|Elhalawani, Hesham; Lin, Timothy A; Volpe, Stefania et al. (2018) Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges. Front Oncol 8:294|
|Chang, Ken; Bai, Harrison X; Zhou, Hao et al. (2018) Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging. Clin Cancer Res 24:1073-1081|
|Joint Head and Neck Radiotherapy-MRI Development Cooperative (2018) Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers. Sci Data 5:180008|
|Zhou, M; Scott, J; Chaudhury, B et al. (2018) Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. AJNR Am J Neuroradiol 39:208-216|
|Schmainda, K M; Prah, M A; Rand, S D et al. (2018) Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. AJNR Am J Neuroradiol 39:1008-1016|
Showing the most recent 10 out of 61 publications