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使用光学相干断层扫描技术评估淋巴水肿：大鼠肢体模型方法

An Evaluation of Lymphedema Using Optical Coherence Tomography: A Rat Limb Model Approach.

作者信息

Nikolaev V V, Trimassov I A, Amirchanov D S, Shirshin E A, Krivova N A, Beliaeva S A, Sandykova E A, Kistenev Yu V

机构信息

Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36, Lenin Ave., Tomsk 634050, Russia.

Faculty of Physics, Lomonosov Moscow State University, Moscow 119991, Russia.

出版信息

Diagnostics (Basel). 2023 Aug 31;13(17):2822. doi: 10.3390/diagnostics13172822.

DOI:10.3390/diagnostics13172822
PMID:37685360
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10486677/
Abstract

Lymphedema is a pathology caused by poor lymphatic flow which may lead to complete disability. Currently, precise, non-invasive techniques for quantifying lymphedema are lacking. In this paper, the results of an in vivo assessment of lymphedema via a developed small-animal model using the hindlimbs of rats and an optical coherence tomography (OCT) technique are presented. This model of lymphedema was based on a surgical lymph node resection and subsequent two-step X-ray exposure. The development of lymphedema was verified via the histological examination of tissue biopsies. The properties of the lymphedematous skin were analyzed in vivo and compared with healthy skin via OCT. The main differences observed were (1) a thickening of the stratum corneum layer, (2) a thinning of the viable epidermis layer, and (3) higher signal attenuation in the dermis layer of the lymphedematous skin. Based on the distribution of the OCT signal's intensity in the skin, a machine learning algorithm was developed which allowed for a classification of normal and lymphedematous tissue sites with an accuracy of 90%. The obtained results pave the way for in vivo control over the development of lymphedema.

摘要

淋巴水肿是一种由淋巴液流动不畅引起的病理状况，可能导致完全残疾。目前，缺乏精确的、非侵入性的淋巴水肿量化技术。本文介绍了通过使用大鼠后肢和光学相干断层扫描（OCT）技术开发的小动物模型对淋巴水肿进行体内评估的结果。这种淋巴水肿模型基于手术切除淋巴结并随后进行两步X射线照射。通过组织活检的组织学检查验证了淋巴水肿的发展。通过OCT在体内分析了淋巴水肿皮肤的特性，并与健康皮肤进行了比较。观察到的主要差异为：（1）角质层增厚；（2）活表皮层变薄；（3）淋巴水肿皮肤的真皮层信号衰减更高。基于皮肤中OCT信号强度的分布，开发了一种机器学习算法，该算法能够以90%的准确率对正常组织部位和淋巴水肿组织部位进行分类。所获得的结果为体内控制淋巴水肿的发展铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/eb43af20828f/diagnostics-13-02822-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/aa10d24b670a/diagnostics-13-02822-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/78abd2f6d3a5/diagnostics-13-02822-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/ccea016465de/diagnostics-13-02822-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/6fa22742b941/diagnostics-13-02822-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/133c1279fa10/diagnostics-13-02822-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/73dd2be9082b/diagnostics-13-02822-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/a787d37232e6/diagnostics-13-02822-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/d504072ab13d/diagnostics-13-02822-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/eb43af20828f/diagnostics-13-02822-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/aa10d24b670a/diagnostics-13-02822-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/78abd2f6d3a5/diagnostics-13-02822-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/ccea016465de/diagnostics-13-02822-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/6fa22742b941/diagnostics-13-02822-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/133c1279fa10/diagnostics-13-02822-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/73dd2be9082b/diagnostics-13-02822-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/a787d37232e6/diagnostics-13-02822-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/d504072ab13d/diagnostics-13-02822-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5340/10486677/eb43af20828f/diagnostics-13-02822-g009.jpg

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Pharmaceutics. 2022 Feb 11;14(2):399. doi: 10.3390/pharmaceutics14020399.
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A Murine Tail Lymphedema Model.一种鼠类尾部淋巴水肿模型。
J Vis Exp. 2021 Feb 10(168). doi: 10.3791/61848.
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Evidence of stage progression in a novel, validated fluorescence-navigated and microsurgical-assisted secondary lymphedema rodent model.
新型荧光导航和显微辅助二级淋巴水肿啮齿动物模型中分期进展的证据。
PLoS One. 2020 Jul 23;15(7):e0235965. doi: 10.1371/journal.pone.0235965. eCollection 2020.
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Creation of a Rat Lower Limb Lymphedema Model.大鼠下肢淋巴水肿模型的建立。
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