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Biomedical subjects

Sarah Lenington

Publications and source records attributed to Sarah Lenington.

2 recordsLinked to original sources

Breast electrical impedance and estrogen use in postmenopausal women.

OBJECTIVES: To examine the potential for using electrical impedance measurements on the breast as an indicator of estrogen activity in breast tissue. METHODS: Eighty-six postmenopausal women were examined with TS2000, a device that measures electrical capacitance and conductance on the breast. Seventy women had undergone natural menopause (NM) and 16 had had a hysterectomy/ovarectomy. Twenty-one women were using estrogen replacement therapy (ERT). Each woman had electrical impedance measured over several frequencies on the nipple sector of both breasts. We analyzed capacitance and conductance at 200 and 1100 Hz and the slopes and intercepts of regression lines relating capacitance and conductance to the natural log of frequency. RESULTS: Type of menopause (natural or induced) was not statistically related to any measured variable. The number of years since the start of menopause was statistically related to all measured variables. Overall, levels of capacitance and conductance decreased as the number of years since the start of menopause increased. Women who used ERT had a statistically higher level of nipple conductance at 200 Hz than did women who did not use ERT. CONCLUSIONS: The pattern of electrical measurements on the nipple (and in particular conductance at 200 Hz) is correlated with the pattern of estrogen changes after menopause. These data indicate that electrical measurements might be a useful, non-invasive assay of estrogen activity in breast tissue.

Breast↗

Novel EIS postprocessing algorithm for breast cancer diagnosis.

A new postprocessing algorithm was developed for the diagnosis of breast cancer using electrical impedance scanning. This algorithm automatically recognizes bright focal spots in the conductivity map of the breast. Moreover, this algorithm discriminates between malignant and benign/normal tissues using two main predictors: phase at 5 kHz and crossover frequency, the frequency at which the imaginary part of the admittance is at its maximum. The thresholds for these predictors were adjusted using a learning group consisting of 83 carcinomas and 378 benign cases. In addition, the algorithm was verified on an independent test group including 87 carcinomas, 153 benign cases and 356 asymptomatic cases. Biopsy was used as gold standard for determining pathology in the symptomatic cases. A sensitivity of 84% and a specificity of 52% were obtained for the test group.

Algorithms↗