RESUMO
There are no currently available low-cost, noninvasive methods for discerning the depth of squamous cell carcinoma (SCC) invasion or distinguishing SCC from its benign mimics, such as inflamed seborrheic keratosis (SK). We studied 35 subjects with subsequently confirmed SCC or SK. Subjects underwent electrical impedance dermography measurements at six frequencies to assess the electrical properties of the lesion. Averaged greatest intrasession reproducibility values were 0.630 for invasive SCC at 128 kHz, 0.444 for SCC in situ at 16 kHz, and 0.460 for SK at 128 kHz. Electrical impedance dermography modeling revealed significant differences between SCC and inflamed SK in normal skin (P < 0.001) and also between invasive SCC and SCC in situ (P < 0.001), invasive SCC and inflamed SK (P < 0.001), and SCC in situ and inflamed SK (P < 0.001). A diagnostic algorithm classified SCC in situ from inflamed SK with an accuracy of 0.958, a sensitivity of 94.6%, and a specificity of 96.9%; it also classified SCC in situ from normal skin with an accuracy of 0.796, a sensitivity of 90.2%, and a specificity of 51.2%. This study provides preliminary data and a methodology that can be used in future studies to further advance the value of electrical impedance dermography and inform biopsy decision making in patients with lesions suspicious of SCC.
RESUMO
Skin changes associated with alterations in the interstitial matrix and lymph system might provide significant and measurable effects due to the presence of breast cancer. This study aimed to determine if skin electrical resistance changes could serve as a diagnostic and therapeutic biomarker associated with physiological changes in patients with malignant versus benign breast cancer lesions. Forty-eight women (24 with malignant cancer, 23 with benign lesions) were enrolled in this study. Repeated skin resistance measurements were performed within the same session and 1 week after the first measurement in the breast lymphatic region and non-breast lymphathic regions. Intraclass correlation coefficients were calculated to determine the technique's intrasession and intersession reproducibility. Data were then normalized as a mean of comparing cross-sectional differences between malignant and benign lesions of the breast. Six months longitudinal data from six patients that received therapy were analyzed to detect the effect of therapy. Standard descriptive statistics were used to compare ratiometric differences between groups. Skin resistance data were used to train a machine learning random forest classification algorithm to diagnose breast cancer lesions. Significant differences between malignant and benign breast lesions were obtained (p<0.01), also pre- and post-treatment (p<0.05). The diagnostic algorithm demonstrated the capability to classify breast cancer with an area under the curve of 0.68, sensitivity of 66.3%, specificity of 78.5%, positive predictive value 70.7% and negative predictive value 75.1%. Measurement of skin resistance in patients with breast cancer may serve as a convenient screening tool for breast cancer and evaluation of therapy. Further work is warranted to improve our approach and further investigate the biophysical mechanisms leading to the observed changes.
