基于光发射光谱的实时组织分化用于引导性电外科肿瘤切除术。
Real-time tissue differentiation based on optical emission spectroscopy for guided electrosurgical tumor resection.
作者信息
Spether Dominik, Scharpf Marcus, Hennenlotter Jörg, Schwentner Christian, Neugebauer Alexander, Nüßle Daniela, Fischer Klaus, Zappe Hans, Stenzl Arnulf, Fend Falko, Seifert Andreas, Enderle Markus
机构信息
Gisela and Erwin Sick Chair of Micro-optics, Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany.
Institute of Pathology and Neuropathology, University Hospital Tuebingen, 72076 Tuebingen, Germany.
出版信息
Biomed Opt Express. 2015 Mar 24;6(4):1419-28. doi: 10.1364/BOE.6.001419. eCollection 2015 Apr 1.
Complete surgical removal of cancer tissue with effective preservation of healthy tissue is one of the most important challenges in modern oncology. We present a method for real-time, in situ differentiation of tissue based on optical emission spectroscopy (OES) performed during electrosurgery not requiring any biomarkers, additional light sources or other excitation processes. The analysis of the optical emission spectra, enables the differentiation of healthy and tumorous tissue. By using multi-class support vector machine (SVM) algorithms, distinguishing between tumor types also seems to be possible. Due to its fast reaction time (0.05s) the method can be used for real-time navigation helping the surgeon achieve complete resection. The system's easy realization has been proven by successful integration in a commercial electro surgical unit (ESU). In a first step the method was verified by using ex vivo tissue samples. The histological analysis confirmed, 95% of correctly classified tissue types.
在有效保留健康组织的同时完整手术切除癌组织是现代肿瘤学中最重要的挑战之一。我们提出了一种基于电外科手术期间进行的光发射光谱（OES）的实时原位组织分化方法，该方法不需要任何生物标志物、额外光源或其他激发过程。对光发射光谱的分析能够区分健康组织和肿瘤组织。通过使用多类支持向量机（SVM）算法，区分肿瘤类型似乎也是可行的。由于其快速的反应时间（0.05秒），该方法可用于实时导航，帮助外科医生实现完整切除。该系统易于实现已通过成功集成到商业电外科设备（ESU）中得到证明。第一步，该方法通过使用离体组织样本进行了验证。组织学分析证实，95%的组织类型分类正确。
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