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Breast cancer detection and differen...

Drexel University.

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Breast cancer detection and differentiation using piezoelectric fingers.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Breast cancer detection and differentiation using piezoelectric fingers./
作者:	Yegingil, Hakki Orhan.
面頁冊數:	227 p.
附註:	Advisers: Wan Y. Shih; Wei-Heng Shih.
Contained By:	Dissertation Abstracts International70-01B.
標題:	Biophysics, Medical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3342768
ISBN:	9780549980018

Breast cancer detection and differentiation using piezoelectric fingers.
Yegingil, Hakki Orhan.

Breast cancer detection and differentiation using piezoelectric fingers. - 227 p.

Advisers: Wan Y. Shih; Wei-Heng Shih.

Thesis (Ph.D.)--Drexel University, 2009.

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 &sim;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.

ISBN: 9780549980018Subjects--Topical Terms:

1017681
Biophysics, Medical.

Breast cancer detection and differentiation using piezoelectric fingers.
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502 $a Thesis (Ph.D.)--Drexel University, 2009.
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