Cole Relaxation Frequency as a Prognostic Parameter for Breast Cancer.

We previously reported successful classification of breast cancer versus benign tissue using the Cole relaxation frequency measured on tissue excised during breast surgery as part of a study at two urban hospitals in the U.S. Midwest. Using that health system's cancer registry, we have discovered retrospectively that outcomes for patients who participated in the initial study can be classified correctly in 3 well-differentiated categories: nonrecurrent (NR); recurrent with no metastasis (RNM); and recurrent with metastasis (RM). As Cole relaxation frequency increases, the classification moves from NR to RNM and finally to RM. Multivariate analysis showed a significant association of "time-cancer-free" for all patients in these recurrent categories, with P-values ranging between 0.0001 to 0.0047. Thus, this follow-up report shows the potential feasibility of using Cole relaxation frequency as a prognostic parameter in a larger prospective study.

Efficient Cancer Detection Using Multiple Neural Networks.

The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.