傅立叶变换拉曼光谱与机器学习和多元分析相结合,作为脑肿瘤的诊断工具。

IF 4.2 2区 医学 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Bartłomiej Tołpa MSc , Wiesław Paja Dr , Elżbieta Trojnar MSc , Kornelia Łach MSc , Agnieszka Gala-Błądzińska Dr , Aneta Kowal MSc , Ewelina Gumbarewicz Dr , Paulina Frączek MSc , Józef Cebulski Dr , Joanna Depciuch Dr
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引用次数: 0

摘要

脑肿瘤是最危险的肿瘤之一,因为它们位于支配所有生命过程的器官中。此外,人们还观察到许多脑肿瘤类型,但只使用一种主要诊断方法--组织病理学,而准备样本的时间很长。因此,需要一种新的、更快捷的诊断方法。本文通过主成分分析(PCA)、层次聚类分析(HCA)和四种不同的机器学习(ML)算法对脑组织的傅立叶变换拉曼光谱进行了分析,以显示区分 G4 型胶质母细胞瘤和脑膜瘤以及两种不同类型脑膜瘤(非典型和血管瘤)的可能性。研究结果表明,与 G4 型胶质母细胞瘤相比,脑膜瘤在 1503 cm-1 附近出现了额外的峰值,酰胺含量更高。在脑膜瘤分化方面,血管瘤型脑膜瘤组织中的脂质和多糖含量低于非典型脑膜瘤。此外,PCA 分析表明,在 800 厘米-1 至 1800 厘米-1 的傅立叶变换拉曼光谱范围内,胶质母细胞瘤 G4 和脑膜瘤之间的区分度较高,而在 2700 厘米-1 至 3000 厘米-1 的范围内,两种类型的脑膜瘤之间的区分度较高。决策树显示,区分胶质母细胞瘤和脑膜瘤最重要的峰值是 1151 cm-1 和 2836 cm-1,而区分血管瘤和非典型脑膜瘤最重要的峰值是 1514 cm-1 和 2875 cm-1。此外,G4 型胶质母细胞瘤和脑膜瘤的准确率为 88%,脑膜瘤的准确率为 92%。因此,获得的数据表明傅立叶变换拉曼光谱可用于诊断不同类型的脑肿瘤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors

FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors

Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used – histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm−1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm−1 and 1800 cm−1 and between two types of meningiomas in the range between 2700 cm−1 and 3000 cm−1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm−1 and 2836 cm−1 while for angiomatous and atypical meningiomas – 1514 cm−1 and 2875 cm−1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas – 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors.

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来源期刊
CiteScore
11.10
自引率
0.00%
发文量
133
审稿时长
42 days
期刊介绍: The mission of Nanomedicine: Nanotechnology, Biology, and Medicine (Nanomedicine: NBM) is to promote the emerging interdisciplinary field of nanomedicine. Nanomedicine: NBM is an international, peer-reviewed journal presenting novel, significant, and interdisciplinary theoretical and experimental results related to nanoscience and nanotechnology in the life and health sciences. Content includes basic, translational, and clinical research addressing diagnosis, treatment, monitoring, prediction, and prevention of diseases.
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