基于光谱分析的颅内肿瘤分类

I. D. Romanishkin, T. A. Savelieva, A. Ospanov, K. G. Linkov, S. V. Shugai, S. A. Goryajnov, G. V. Pavlova, I. N. Pronin, V. B. Loschenov
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引用次数: 0

摘要

本研究的动机是需要开发颅内肿瘤切除手术中紧急术中活检的方法。基于以往GPI RAS与N.N. Burdenko国家神经外科医学研究中心联合工作的经验,将荧光光谱方法引入临床实践,结合自身荧光光谱、5-ALA诱导原卟啉IX荧光、宽带光漫反射等多种光谱技术,可用于测定组织中血红蛋白浓度及其光密度、拉曼光谱、这是一种光谱方法,可以通过单个特征分子键的振动来检测组织中的各种分子。如此多样的光学和光谱特征使得外科医生在手术中难以直接对其进行分析,因为在荧光方法中通常实现的是通过超过阈值的荧光强度可以在一定程度上确定地将肿瘤组织与正常组织区分开来。当参数数量超过几十个时,有必要使用机器学习算法为外科医生构建术中决策支持系统。本文提出了这方面的研究。我们早期对光谱特征的统计分析允许识别用于诊断重要组织成分分析的统计显著光谱范围。对光谱特征向量降维技术和样本聚类方法的研究也使我们接近了自动分类方法的实现。重要的是,分类任务可用于两种应用-区分不同的肿瘤和区分同一肿瘤的不同部分(中心,焦周区,正常)。本文介绍了第一个方向的研究成果。我们研究了几种方法的组合,并展示了基于所提出的光谱分析方法区分神经胶质瘤和脑膜瘤的可能性。
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Classification of intracranial tumors based on optical-spectral analysis
The motivation for the present study was the need to develop methods of urgent intraoperative biopsy during surgery for removal of intracranial tumors. Based on the experience of previous joint work of GPI RAS and N.N. Burdenko National Medical Research Center of Neurosurgery to introduce fluorescence spectroscopy methods into clinical practice, an approach combining various optical-spectral techniques, such as autofluorescence spectroscopy, fluorescence of 5-ALA induced protoporphyrin IX, diffuse reflection of broadband light, which can be used to determine hemoglobin concentration in tissues and their optical density, Raman spectroscopy, which is a spectroscopic method that allows detection of various molecules in tissues by vibrations of individual characteristic molecular bonds. Such a variety of optical and spectral characteristics makes it difficult for the surgeon to analyze them directly during surgery, as it is usually realized in the case of fluorescence methods – tumor tissue can be distinguished from normal with a certain degree of certainty by fluorescence intensity exceeding a threshold value. In case the number of parameters exceeds a couple of dozens, it is necessary to use machine learning algorithms to build a intraoperative decision support system for the surgeon. This paper presents research in this direction. Our earlier statistical analysis of the optical-spectral features allowed identifying statistically significant spectral ranges for analysis of diagnostically important tissue components. Studies of dimensionality reduction techniques of the optical-spectral feature vector and methods of clustering of the studied samples also allowed us to approach the implementation of the automatic classification method. Importantly, the classification task can be used in two applications – to differentiate between different tumors and to differentiate between different parts of the same (center, perifocal zone, normal) tumor. This paper presents the results of our research in the first direction. We investigated the combination of several methods and showed the possibility of differentiating glial and meningeal tumors based on the proposed optical-spectral analysis method.
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来源期刊
Biomedical Photonics
Biomedical Photonics Medicine-Surgery
CiteScore
1.80
自引率
0.00%
发文量
19
审稿时长
8 weeks
期刊介绍: The main goal of the journal – to promote the development of Russian biomedical photonics and implementation of its advances into medical practice. The primary objectives: - Presentation of up-to-date results of scientific and in research and scientific and practical (clinical and experimental) activity in the field of biomedical photonics. - Development of united Russian media for integration of knowledge and experience of scientists and practitioners in this field. - Distribution of best practices in laser medicine to regions. - Keeping the clinicians informed about new methods and devices for laser medicine - Approval of investigations of Ph.D candidates and applicants.
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