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用于改善皮肤癌诊断的光学技术:综述

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review.

作者信息

Rey-Barroso Laura, Peña-Gutiérrez Sara, Yáñez Carlos, Burgos-Fernández Francisco J, Vilaseca Meritxell, Royo Santiago

机构信息

Centre for Sensors, Instruments and Systems Development, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain.

出版信息

Sensors (Basel). 2021 Jan 2;21(1):252. doi: 10.3390/s21010252.

Abstract

The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.

摘要

在过去几十年中,皮肤癌的全球发病率迅速上升,如今已成为三大癌症之一。目前,一个人患黑色素瘤(最具侵袭性的皮肤癌形式,导致死亡人数最多)的几率为4%。在发病率和死亡率不断上升的背景下,皮肤癌带来了沉重的健康和经济负担。然而,如果皮肤癌能早期被发现并治疗,患者的5年生存率会显著提高。因此,人们投入了大量研究工作以实现早期检测并更好地了解这种疾病,目的是扭转发病率和死亡率不断上升的趋势,尤其是针对黑色素瘤。本文综述了近年来为改善皮肤癌非侵入性诊断而使用的各种光学模态,包括共聚焦显微镜、多光谱成像、三维形貌、光学相干断层扫描、偏振测量、自混合干涉测量以及机器学习算法。描述了这些技术各自的基础以及取得的最相关成果,还有一些仍有待解决的障碍和要达成的里程碑。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67e1/7795742/ccdf8165005e/sensors-21-00252-g001.jpg

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