Department of Nephrology, Department of Internal Medicine and Pediatrics, Ghent University Hospital, Ghent, Belgium.
Department of Diagnostic Sciences, Ghent University, Ghent, Belgium.
Commun Biol. 2024 Oct 31;7(1):1419. doi: 10.1038/s42003-024-07111-7.
The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, current conventional diagnostic methods are often found only in specialized environments. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy offers a nondestructive and user-friendly approach in the analysis of a wide range of samples. In this paper, we determined whether the technique coupled with machine learning can detect and differentiate lymphoma within lymphoid tissue samples. Tissue sections from 295 individuals diagnosed with lymphoma and 389 individuals without the disease were analyzed using ATR-FTIR spectroscopy. The resulting spectral dataset was split using a 70:30 train-test split. Partial least Squares Discriminant Analysis (PLS-DA) models were trained to distinguish non-malignant lymphoid tissue from lymphoma samples and to differentiate between subtypes. On the training set (n = 478), significant spectral differences were mainly identified in the 1800-900 cm region, attributed to fundamental biochemical constituents like proteins, lipids, carbohydrates, and nucleic acids. On the independent test set (n = 206), the trained PLS-DA model achieved a promising AUC of 0.882 (95% CI: 0.881-0.884) in the differentiation between lymphoma and non-malignant lymphoid tissue. In addition, comparative analyses revealed spectral distinctions and notable clustering between the different lymphoma subtypes. This study provides valuable insights into the application of ATR-FTIR spectroscopy and machine learning in the field of lymphoma diagnosis as a non-destructive, rapid and inexpensive tool with the potential to be easily implemented in non-specialized laboratories.
淋巴瘤的诊断具有挑战性，因为它们具有多样化的组织学表现和临床表现。需要有一种廉价的工具，这种工具不需要专业知识，并且可以在常规实验室中使用。相比之下，当前的常规诊断方法通常只在专门的环境中找到。衰减全反射-傅里叶变换红外（ATR-FTIR）光谱分析提供了一种在分析广泛样本时既非破坏性又用户友好的方法。在本文中，我们确定了该技术是否与机器学习相结合，可以检测和区分淋巴组织样本中的淋巴瘤。使用 ATR-FTIR 光谱分析了 295 名被诊断患有淋巴瘤的个体和 389 名未患该病的个体的组织切片。将得到的光谱数据集使用 70:30 的训练-测试分割进行分割。训练了偏最小二乘判别分析（PLS-DA）模型，以区分非恶性淋巴组织和淋巴瘤样本，并区分亚型。在训练集（n=478）上，主要在 1800-900 cm 区域识别出显著的光谱差异，这归因于蛋白质、脂质、碳水化合物和核酸等基本生物化学成分。在独立的测试集（n=206）上，训练有素的 PLS-DA 模型在区分淋巴瘤和非恶性淋巴组织方面达到了 0.882（95%CI：0.881-0.884）的有希望的 AUC。此外，比较分析显示了不同淋巴瘤亚型之间的光谱差异和显著聚类。这项研究为 ATR-FTIR 光谱分析和机器学习在淋巴瘤诊断领域的应用提供了有价值的见解，作为一种非破坏性、快速且廉价的工具，具有在非专业实验室中易于实施的潜力。
