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.

Figure 1

(a) Mean blood flow values for all recording locations and groups of studied lesions. Data are presented as boxplots where the upper and lower limits of each box represent the 75^th and 25^th percentiles, respectively; the line between these is the median value. Outliers are shown in red. (b) Intra-lesion micro-vessels highlighted with CD34 Mab (Ventan Medical System) at the level of a skin malignant melanoma. (c) Results of micro-vessel count for all examined groups. (d–f) show the correlation found for all malignant melanomas studied between vessel count and mean blood flow detected at the level of the lesion center (d), between lesion area and mean blood flow detected at the level of the lesion center (e), and between vessel count and lesion area (f). *p < 0.05; ***p < 0.001; PU = perfusion unit.

Figure 2

(a–c) Normalized median spectral power values obtained by wavelet analysis of laser Doppler tracings recorded for 30 minutes in SMM lesions (red line), atypical nevi (dark grey line), benign nevi (light grey line) and psoriasis (black line) in lesions centers (a), margins (b) and in contralateral healthy skin (c). Significant differences are highlighted in yellow (p < 0.05) as determined by the Kruskal Wallis test. (d–f) Normalized spectral power values within the six frequency intervals considered (see text) in SMM (red), atypical nevi (dark grey), benign nevi (light grey) and psoriasis (black) in lesion centers (d), margins (e) and healthy contralateral skin (f). *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 3

(a–c) – boxplots of the three values used in the diagnostic test (left, p values 0.0009, 0.0035 & 0.0001, respectively) and sensitivities and specificities with varying thresholds (right). (d) Scores obtained from the diagnostic test (where 0 = melanoma), based on threshold values of 1.26, 0.0038 and 3.7.

Figure 4

(a) A laser Doppler probe placed above a skin malignant melanoma. Skin malignant melanoma microvasculature and laser-Doppler effect are schematically shown. (b) Typical blood flow signals recorded from the center of a clinically benign skin nevus (black line) modulated by respiration (red line). (c) Continuous wavelet transform representation of the laser Doppler signal recorded from the center of a clinically benign skin nevus. (d) Time averaged wavelet transform representation of the laser Doppler signals recorded from the center of a clinically benign skin nevus (black line) and from the contralateral healthy skin site of the same subject (red line). This allows accurate visualization of the frequency content of the I-VI intervals defined in the Methods section.