There is an urgent need for cost-effective, non-invasive diagnostic techniques for the evaluation of breast lesions. The primary objective of this project is to develop a multiparameter MRI based tissue model to differentiate benign from malignant tissue that may be used for the purpose of selecting treatment options. While many imaging techniques (particularly contrast-enhanced MRI) now provide high sensitivity for the detection of breast lesions, specificity remains relatively low, resulting in many biopsies of lesions that have a benign final diagnosis. Our proposed approach investigates a mechanistic hypothesis that multiparameter MRI, by accounting for the multiple biophysical states of water in tissue and tissue perfusion, _ yield a more complete classification of breast tissue than any single MRI parameter. Potentially malignant tissue can be identified by combining multiparameter MRI data [Tl-weighted (T1WI), T2-weighted (T2WI), pre- and post- dynamic contrast enhanced gadolinium (GdDTPA), soditma, and spectroscopic MR images] in conjunction with cluster analysis methods (ISODATA and angle analysis) into a single vector, to improve differentiation between benign or malignant breast lesions and breast tissue. Based on our development of MRI based tissue models to differentiate and diagnose breast cancer, we propose to accomplish these objectives using a multiparameter MRI data approach in conjunction with sophisticated data analysis routines. This approach Hill enable us to monitor and predict which patients will respond to chemotherapy and those patients that will not respond by monitoring the ISODATA vector characteristics of the breast tissue in chemotherapy.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Diagnostic Imaging Study Section (DMG)
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Zhang, Huiming
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Johns Hopkins University
Schools of Medicine
United States
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Akhbardeh, Alireza; Jacobs, Michael A (2012) Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation. Med Phys 39:2275-89
Fayad, Laura M; Jacobs, Michael A; Wang, Xin et al. (2012) Musculoskeletal tumors: how to use anatomic, functional, and metabolic MR techniques. Radiology 265:340-56
Subhawong, Ty K; Wang, Xin; Durand, Daniel J et al. (2012) Proton MR spectroscopy in metabolic assessment of musculoskeletal lesions. AJR Am J Roentgenol 198:162-72
Jacobs, Michael A; Ouwerkerk, Ronald; Wolff, Antonio C et al. (2011) Monitoring of neoadjuvant chemotherapy using multiparametric, ²³Na sodium MR, and multimodality (PET/CT/MRI) imaging in locally advanced breast cancer. Breast Cancer Res Treat 128:119-26
Bonekamp, David; Jacobs, Michael A; El-Khouli, Riham et al. (2011) Advancements in MR imaging of the prostate: from diagnosis to interventions. Radiographics 31:677-703
Harouni, Ahmed A; Hossain, Jakir; Jacobs, Michael A et al. (2011) Improved hardware for higher spatial resolution strain-encoded (SENC) breast MRI for strain measurements. Acad Radiol 18:705-15
El Khouli, Riham H; Macura, Katarzyna J; Kamel, Ihab R et al. (2011) 3-T dynamic contrast-enhanced MRI of the breast: pharmacokinetic parameters versus conventional kinetic curve analysis. AJR Am J Roentgenol 197:1498-505
Kim, Hyun S; Baik, Jun-Hyun; Pham, Luu D et al. (2011) MR-guided high-intensity focused ultrasound treatment for symptomatic uterine leiomyomata: long-term outcomes. Acad Radiol 18:970-6
Penet, Marie-France; Winnard Jr, Paul T; Jacobs, Michael A et al. (2011) Understanding cancer-induced cachexia: imaging the flame and its fuel. Curr Opin Support Palliat Care 5:327-33
Harouni, Ahmed A; Jacobs, Michael A; Osman, Nael F (2011) Finding the optimal compression level for strain-encoded (SENC) breast MRI; simulations and phantom experiments. Med Image Comput Comput Assist Interv 14:444-51

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