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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/2969

Title: Breast cancer detection and differentiation using piezoelectric fingers
Authors: Yegingil, Hakki Orhan
Keywords: Materials science;Piezoelectric materials;Breast -- Cancer
Issue Date: 18-Feb-2009
Abstract: A piezoelectric finger (PEF) is a tissue elasticity sensor developed in our laboratory. With a dual piezoelectric layer design, a PEF can apply a force and detect the resultant displacement all‐electrically, ideal for potential in vivo tissue elasticity measurements. The goal of this thesis is to develop PEFs towards a breast cancer detector. The study encompasses (1) fundamental development and characterization of PEFs as a tissue elasticity sensor using model tissues, (2) application of PEFs to ex vivo breast samples, and (3) development of array PEFs towards in vivo measurements. I have shown that a PEF can accurately measure the elastic or shear moduli values of soft polymer samples using indentation methods. Furthermore, I have shown that a PEF has a depth sensitivity twice its width by testing inclusions embedded at various depths in model tissues. Using the measurements from two PEFs of different widths, I showed that the depth and modulus of an inclusion can be determined with an empirical “two‐spring” model. I have shown that a PEF could distinguish between the 2‐D and 3‐D smooth and rough surface inclusions by examining the shear (G) to elastic (E) moduli ratio: smooth and rough inclusions have G/E ratio of ~0.3 and >0.7, respectively. I have characterized 71 ex vivo breast tumors in terms of tumor size, location, malignancy and invasiveness. I have shown that PEFs predicted all abnormalities, including a 3 mm tumor. PEF’s size predictions were accurate within 10% of the pathologic measurements. Furthermore, using G/E > 0.7 as a criterion, we predicted invasive carcinoma with 89% sensitivity and 82% specificity. With G/E = 0.3 and >0.7 as a criterion, the malignancy prediction had a 96% sensitivity and 54% specificity. Moving toward real patient applications, PEF compression array was developed, characterized over the model tissue samples, and successfully located an in‐vivo tumor inside breast tissue and predicted its size, depth and modulus. Instantaneous moduli measurement and PEF array motion automation were achieved.
URI: http://hdl.handle.net/1860/2969
Appears in Collections:Drexel Theses and Dissertations

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