University of Missouri, Department of Dermatology, Columbia, Missouri 65211, USA.
J Biomed Opt. 2010 Jan-Feb;15(1):016019. doi: 10.1117/1.3316297.
Discrimination of pigmented and vascular lesions in skin can be difficult due to factors such as size, subungual location, and the nature of lesions containing both melanin and vascularity. Misdiagnosis may lead to precancerous or cancerous lesions not receiving proper medical care. To aid in the rapid and accurate diagnosis of such pathologies, we develop a photoacoustic system to determine the nature of skin lesions in vivo. By irradiating skin with two laser wavelengths, 422 and 530 nm, we induce photoacoustic responses, and the relative response at these two wavelengths indicates whether the lesion is pigmented or vascular. This response is due to the distinct absorption spectrum of melanin and hemoglobin. In particular, pigmented lesions have ratios of photoacoustic amplitudes of approximately 1.4 to 1 at the two wavelengths, while vascular lesions have ratios of about 4.0 to 1. Furthermore, we consider two statistical methods for conducting classification of lesions: standard multivariate analysis classification techniques and a Bayesian-model-based approach. We study 15 human subjects with eight vascular and seven pigmented lesions. Using the classical method, we achieve a perfect classification rate, while the Bayesian approach has an error rate of 20%.
由于大小、甲下位置以及包含黑色素和血管的病变性质等因素，皮肤中色素性和血管性病变的鉴别可能较为困难。误诊可能导致未接受适当医疗护理的癌前或癌变病变。为了帮助快速准确地诊断此类病变，我们开发了一种光声系统来确定体内皮肤病变的性质。通过用 422nm 和 530nm 两种激光波长照射皮肤，我们诱导光声响应，这两种波长的相对响应表明病变是色素性还是血管性。这种响应是由于黑色素和血红蛋白的独特吸收光谱所致。具体而言，色素性病变在两个波长处的光声幅度比约为 1.4 比 1，而血管性病变的比约为 4.0 比 1。此外，我们考虑了两种用于进行病变分类的统计方法：标准多元分析分类技术和基于贝叶斯模型的方法。我们研究了 15 名人类受试者的 8 个血管性病变和 7 个色素性病变。使用经典方法，我们实现了完美的分类率，而贝叶斯方法的错误率为 20%。
