基于激光诱导击穿光谱法的甲状旁腺识别初步研究。
Primary study of identification of parathyroid gland based on laser-induced breakdown spectroscopy.
作者信息
Wang Qianqian, Xiangli Wenting, Chen Xiaohong, Zhang Jinghong, Teng Geer, Cui Xutai, Idrees Bushra Sana, Wei Kai
机构信息
School of Optics and Photonics, Beijing Institute of Technology, 100081 Beijing, China.
Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, 100081 Beijing, China.
出版信息
Biomed Opt Express. 2021 Mar 10;12(4):1999-2014. doi: 10.1364/BOE.417738. eCollection 2021 Apr 1.
The identification and preservation of parathyroid glands (PGs) is a major issue in thyroidectomy. The PG is particularly difficult to distinguish from the surrounding tissues. Accidental damage or removal of the PG may result in temporary or permanent postoperative hypoparathyroidism and hypocalcemia. In this study, a novel method for identification of the PG was proposed based on laser-induced breakdown spectroscopy (LIBS) for the first time. LIBS spectra were collected from the smear samples of PG and non-parathyroid gland (NPG) tissues (thyroid and neck lymph node) of rabbits. The emission lines (related to K, Na, Ca, N, O, CN, C, etc.) observed in LIBS spectra were ranked and selected based on the important weight calculated by random forest (RF). Three machine learning algorithms were used as classifiers to distinguish PGs from NPGs. The artificial neural network classifier provided the best classification performance. The results demonstrated that LIBS can be adopted to discriminate between smear samples of PG and NPG, and it has a potential in intra-operative identification of PGs.
甲状旁腺（PG）的识别与保留是甲状腺切除术中的一个主要问题。甲状旁腺特别难以与周围组织区分开来。甲状旁腺的意外损伤或切除可能导致术后暂时或永久性甲状旁腺功能减退和低钙血症。在本研究中，首次提出了一种基于激光诱导击穿光谱（LIBS）识别甲状旁腺的新方法。从兔甲状旁腺和非甲状旁腺（NPG）组织（甲状腺和颈部淋巴结）的涂片样本中收集LIBS光谱。基于随机森林（RF）计算的重要权重对LIBS光谱中观察到的发射线（与K、Na、Ca、N、O、CN、C等有关）进行排序和选择。使用三种机器学习算法作为分类器来区分甲状旁腺和非甲状旁腺。人工神经网络分类器提供了最佳的分类性能。结果表明，LIBS可用于区分甲状旁腺和非甲状旁腺的涂片样本，并且在术中识别甲状旁腺方面具有潜力。
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