Our previous studies suggest that gray-scale ultrasound and Doppler imaging complement one another. While gray-scale imaging can characterize masses with high specificity, Doppler imaging has high sensitivity. This application proposes an innovative integration of the two modes such that both high sensitivity and specificity can be achieved. An intense 5-year multidisciplinary program encompassing three specific goals is proposed.
In Aim 1 color-Doppler, power-Doppler and gray- scale images will be acquired from 300 patients with suspicious breast masses. All modes of imaging will be conducted on the same patients and on the same day. The issues related to optimal imaging will be emphasized by careful control of experimental conditions. Among other things, this will include regular image calibration and monitoring of progesterone levels in the patients to account for variations in blood flow during the normal menstrual cycle as well as for variations due to hormonal therapy.
In Aim 2 we introduce new approaches for deriving quantitative features from Doppler and gray-scale images. The emphasis will be on quantifying features that physicians use in evaluating images.
In Aim 3 these features will be supplemented with qualitative assessments to develop a multifactorial model for a comprehensive diagnosis of breast lesions. Novel approaches based on advanced nonlinear methods, including neural networks, will be developed to formulate a decision tree for cancer diagnosis. Each strategy will be evaluated by ROC analysis. This approach will provide objective measures to demonstrate the importance of sonographic and Doppler features and how they can be used optimally to accurately diagnose breast cancers. Taken together, this program takes advantage of unique breadth of clinical experience and basic sciences at the University of Pennsylvania. An integrated approach in which one mode represents tissue property and the other its function will result in a systematic and comprehensive diagnosis of breast cancers.
| Sehgal, Chandra M; Weinstein, Susan P; Arger, Peter H et al. (2006) A review of breast ultrasound. J Mammary Gland Biol Neoplasia 11:113-23 |
| Song, Jae H; Venkatesh, Santosh S; Conant, Emily A et al. (2005) Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses. Acad Radiol 12:487-95 |
| Weinstein, Susan P; Conant, Emily F; Sehgal, Chandra M et al. (2005) Hormonal variations in the vascularity of breast tissue. J Ultrasound Med 24:67-72; quiz 74 |
| Sehgal, Chandra M; Cary, Theodore W; Kangas, Sarah A et al. (2004) Computer-based margin analysis of breast sonography for differentiating malignant and benign masses. J Ultrasound Med 23:1201-9 |
