Quantitative Analysis of Predictive Markers for Breast Cancer Using Ultrasound Images
Date Issued
2015
Date
2015
Author(s)
Hou, Yu-Ling
Abstract
Breast cancer is the global second-leading cause of cancer death among women. If any possible cancer symptom could be detected in early stages, we can prevent it from getting into an advanced state and reduced lethality. Many studies of predictive markers were proposed in order to inspect breast cancer in early stage. Ultrasound (US) is a simple and effective modality in all non-invasive inspect and plays an important role whether in clinical or research. Although the conventional two-dimensional (2-D) US techniques of the breast at present have been widely used, 2-D images are not enough to transmit the entire characteristics of breast. Therefore, the three-dimensional (3-D) breast US is proposed to improve drawbacks of 2-D breast US. The three-dimensional (3-D) US is also called automated breast ultrasound (ABUS). ABUS can fully provide the architecture of breast in all aspects and therefore is capable of offering a more comprehensive way to characterize pathological features of breast cancer. ABUS is a useful image system, whether it uses in detection, diagnosis or predictive markers in early stage. It provides more detail image information of breast cancer and assists physicians to reduce misdiagnosis. In this study, we present quantitative analysis of predictive markers which include microcalcification and background echotexture for breast cancer using US images. We expected the quantitative analysis of predictive markers for breast cancer could detect the non-palpate breast lesion in early stage in routine examination.
Subjects
breast cancer
predictive markers
microcalcification
background echotexture pattern
SDGs
Type
thesis
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