Klamminger Gilbert Georg, Gérardy Jean-Jacques, Jelke Finn, Mirizzi Giulia, Slimani Rédouane, Klein Karoline, Husch Andreas, Hertel Frank, Mittelbronn Michel, Kleine-Borgmann Felix B
Saarland University Medical Center and Faculty of Medicine, Homburg, Germany.
National Center of Pathology (NCP), Laboratoire national de santé (LNS), Dudelange, Luxembourg.
Neurooncol Adv. 2021 Jun 18;3(1):vdab077. doi: 10.1093/noajnl/vdab077. eCollection 2021 Jan-Dec.
Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" that could be used to differentiate tissue heterogeneity or diagnostic entities. RS has been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS.
To address this issue, we examined FFPE samples of morphologically highly heterogeneous glioblastoma (GBM) using RS in order to classify histologically defined GBM areas according to RS spectral properties. We have set up an SVM (support vector machine)-based classifier in a training cohort and corroborated our findings in a validation cohort.
Our trained classifier identified distinct histological areas such as tumor core and necroses in GBM with an overall accuracy of 70.5% based on the spectral properties of RS. With an absolute misclassification of 21 out of 471 Raman measurements, our classifier has the property of precisely distinguishing between normal-appearing brain tissue and necrosis. When verifying the suitability of our classifier system in a second independent dataset, very little overlap between necrosis and normal-appearing brain tissue can be detected.
These findings show that histologically highly variable samples such as GBM can be reliably recognized by their spectral properties using RS. As conclusion, we propose that RS may serve useful as a future method in the pathological toolbox.
尽管显微镜评估仍是病理学诊断的金标准，但新成像方法和分子病理学等非光学显微镜方法对更精确的诊断有很大贡献。作为一种新兴方法，拉曼光谱（RS）提供了一种“分子指纹”，可用于区分组织异质性或诊断实体。RS已成功应用于新鲜和冷冻组织，然而，对于如福尔马林固定、石蜡包埋（FFPE）样本这类经过更强烈化学处理的组织，RS分析具有挑战性。
为解决这一问题，我们使用RS检查了形态学上高度异质性的胶质母细胞瘤（GBM）的FFPE样本，以便根据RS光谱特性对组织学定义的GBM区域进行分类。我们在一个训练队列中建立了基于支持向量机（SVM）的分类器，并在一个验证队列中证实了我们的发现。
我们训练的分类器根据RS光谱特性识别出GBM中不同的组织学区域，如肿瘤核心和坏死区域，总体准确率为70.5%。在471次拉曼测量中，绝对错误分类为21次，我们的分类器具有精确区分正常脑组织和坏死组织的特性。在第二个独立数据集中验证我们分类器系统的适用性时，坏死组织和正常脑组织之间几乎没有重叠。
这些发现表明，像GBM这样组织学上高度可变的样本可以通过RS的光谱特性可靠地识别。总之，我们认为RS可能作为未来病理工具箱中的一种有用方法。
