Dynamic markers based on blood perfusion fluctuations for selecting skin melanocytic lesions for biopsy

Abstract

Skin malignant melanoma is a highly angiogenic cancer, necessitating early diagnosis for positive prognosis. The current diagnostic standard of biopsy and histological examination inevitably leads to many unnecessary invasive excisions. Here, we propose a non-invasive method of identification of melanoma based on blood flow dynamics. We consider a wide frequency range from 0.005–2 Hz associated with both local vascular regulation and effects of cardiac pulsation. Combining uniquely the power of oscillations associated with individual physiological processes we obtain a marker which distinguishes between melanoma and atypical nevi with sensitivity of 100% and specificity of 90.9%. The method reveals valuable functional information about the melanoma microenvironment. It also provides the means for simple, accurate, in vivo distinction between malignant melanoma and atypical nevi, and may lead to a substantial reduction in the number of biopsies currently undertaken.

Application of cmOCT and continuous wavelet transform analysis to the assessment of skin microcirculation dynamics

“…In this work, the application of CWT on blood flow signals reconstructed from the cmOCT maps allowed the identification of oscillations in the frequency intervals 21 to 52 × 10 −3 Hz, 9.5 to 21 × 10 −3 Hz, and 5 to 9.5 × 10 −3 Hz. According to the literature, 8,30 these oscillations reflect the activity of local sympathetic nerves, the ECs activity nitric oxide (NO)-dependent, and the ECs activity NO-independent.…”

Dynamic evaluation of blood flow microcirculation by combined use of the laser Doppler flowmetry and high‐speed videocapillaroscopy methods

“…However, the analysis and physiological interpretation of the blood flow oscillations remain an open problem for these methods. The blood flow fluctuations are often considered as a source of signal nonrepeatability arising from the stochastic nature of red blood cells (RBCs) velocity fluctuations . At the same time, local spectral decomposition of long‐term perfusion records allows one to reveal the oscillatory components corresponding to the specific physiological mechanisms.…”

“…These mechanisms are considered in detail in refs. . Thus, useful diagnostic information is contained in various spectral bands, and time‐frequency signal analysis is of high importance.…”

Studies of age-related changes in blood perfusion coherence using wearable blood perfusion sensor system

“…There are several frequency ranges that characterise the contribution of various factors to the LDF signal: endothelial (0.0095-0.021 Hz), neurogenic (0.021-0.052 Hz), myogenic (0.052-0.145 Hz), respiratory (0.145-0.6 Hz) and cardiac (0.6-2 Hz). 5 LDF studies are conducted not only at rest, but also with a variety of functional samples, such as local occlusion tests or heating. 6 LDF is also used to study age-related changes in blood flow.…”