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利用傅里叶变换红外光谱和机器学习对胶质母细胞瘤G4和两种脑膜瘤进行鉴别

Differentiation of glioblastoma G4 and two types of meningiomas using FTIR spectra and machine learning.

作者信息

Tołpa Bartłomiej, Paja Wiesław, Jakubczyk Paweł, Łach Kornelia, Trojnar Elżbieta, Gala-Błądzińska Agnieszka, Kowal Aneta, Klębowski Bartosz, Cebulski Jozef, Depciuch Joanna

机构信息

Department of Neurosurgery, Clinical Hospital No 2 in Rzeszow, Poland.

Institute of Computer Science, College of Natural Sciences, University of Rzeszow, Poland.

出版信息

Anal Biochem. 2025 Apr;699:115754. doi: 10.1016/j.ab.2024.115754. Epub 2024 Dec 27.

DOI:10.1016/j.ab.2024.115754
PMID:39734008
Abstract

Brain tumors are among the most dangerous, due to their location in the organ that governs all life processes. Moreover, the high differentiation of these poses a challenge in diagnostics. Therefore, this study focused on the chemical differentiation of glioblastoma G4 (GBM) and two types of meningiomas (atypical - MAtyp and angiomatous - MAng) were done using Fourier Transform InfraRed (FTIR) spectroscopy, combined with statistical, multivariate, machine learning and rate of spectrum changes methods. The positions of all analyzed peaks differed between GBM and meningiomas. However, for two types of meningiomas, only shift of peaks corresponding to CH bending vibrations, symmetric stretching vibrations of CH amide A, amide I, CO lipids vibrations, asymmetric stretching vibrations of CH were observed. Principal Component Analysis showed clear differentiation between GBM and the meningiomas. Decision tree clearly showed that wavenumbers corresponding to CO lipids vibrations provided the highest differentiation between GBM and meningiomas tissues, while amide I for two types of meningiomas. The accuracy and specificity of the results for GBM and meningiomas were more than 90 %, while for MAtyp and MAng, these parameters were around 80 %.

摘要

脑肿瘤是最危险的肿瘤之一,因为它们位于掌管所有生命过程的器官中。此外,这些肿瘤的高度分化给诊断带来了挑战。因此,本研究聚焦于胶质母细胞瘤G4(GBM)的化学分化,并使用傅里叶变换红外(FTIR)光谱结合统计、多变量、机器学习和光谱变化率方法,对两种类型的脑膜瘤(非典型性 - MAtyp和血管瘤性 - MAng)进行了研究。GBM和脑膜瘤之间所有分析峰的位置都不同。然而,对于两种类型的脑膜瘤,仅观察到对应于CH弯曲振动、CH酰胺A对称伸缩振动、酰胺I、CO脂质振动、CH不对称伸缩振动的峰的位移。主成分分析显示GBM和脑膜瘤之间有明显的区分。决策树清楚地表明,对应于CO脂质振动的波数在GBM和脑膜瘤组织之间提供了最高的区分度,而酰胺I则用于区分两种类型的脑膜瘤。GBM和脑膜瘤结果的准确性和特异性超过90%,而对于MAtyp和MAng,这些参数约为80%。

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