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.
One of the problems facing anyone attempting the investigation of dielectric properties of living tissue is the presence of skin, which screens all that lies under it from direct measurement. Thus, in non-invasive breast examination using transimpedance measurements, skin parameters heavily influence the results, specifically at low (less than 10 kHz) frequencies. In this paper a method for overcoming this difficulty by using multi-frequency measurements obtained from a surface current distribution over a flat probe is described. By using the variation in the shape of the real and imaginary parts of the surface current density at different frequencies, the original dielectric values of the skin and the underlying tissue can be obtained, based on the assumption of the existence of a two-layer geometry, with the upper (skin) layer much thinner than the lower (tissue) layer. The results obtained can be used in the diagnosis of breast cancer using existing transimpedance measurement devices.
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