Department of Urology, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, People's Republic of China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Nov 15;281:121603. doi: 10.1016/j.saa.2022.121603. Epub 2022 Jul 7.
To identify and compare the capacities of serum and serum-derived small extracellular vesicles (EV) in diagnosis of common urogenital cancer combining Surface-enhanced Raman spectroscopy (SERS) and Convolutional Neural Networks (CNN).
We collected serum samples from 32 patients with prostate cancer (PCa), 33 patients with renal cell cancer (RCC) and 30 patients with bladder cancer (BCa) as well as 35 healthy control (HC), which were thereafter used to enrich extracellular vesicles by ultracentrifuge. Label-free SERS was utilized to collect Raman spectra from serum and matched EV samples. We constructed CNN models to process SERS data for classification of malignant patients and healthy controls (HCs).
We collected 650 and 1206 spectra from serum and serum-derived EV, respectively. CNN models of EV spectra revealed high testing accuracies of 79.3%, 78.7% and 74.2% in diagnosis of PCa, RCC and BCa, respectively. In comparison, serum SERS-based CNN model had testing accuracies of 73.0%, 71.1%, 69.2% in PCa, RCC and BCa, respectively. Moreover, CNN models based on EV SERS data show significantly higher diagnostic capacities than matched serum CNN models with the area under curve (AUC) of 0.80, 0.88 and 0.74 in diagnosis of PCa, RCC and BCa, respectively.
Deep learning-based SERS analysis of EV has great potentials in diagnosis of urologic cancer outperforming serum SERS analysis, providing a novel tool in cancer screening.
利用表面增强拉曼光谱（SERS）和卷积神经网络（CNN），鉴定和比较血清和血清源性小细胞外囊泡（EV）在联合诊断常见泌尿生殖系统癌中的能力。
我们收集了 32 例前列腺癌（PCa）患者、33 例肾细胞癌（RCC）患者和 30 例膀胱癌（BCa）患者以及 35 例健康对照（HC）的血清样本，然后通过超速离心法从这些样本中富集细胞外囊泡。我们利用无标记 SERS 从血清和匹配的 EV 样本中收集拉曼光谱。我们构建了 CNN 模型，以处理 SERS 数据，用于恶性患者和健康对照（HCs）的分类。
我们分别从血清和血清源性 EV 中收集了 650 和 1206 个光谱。EV 光谱的 CNN 模型在诊断 PCa、RCC 和 BCa 方面的检测准确率分别为 79.3%、78.7%和 74.2%。相比之下，基于血清 SERS 的 CNN 模型在 PCa、RCC 和 BCa 中的检测准确率分别为 73.0%、71.1%和 69.2%。此外，基于 EV SERS 数据的 CNN 模型在诊断 PCa、RCC 和 BCa 方面的诊断能力明显高于匹配的血清 CNN 模型，其曲线下面积（AUC）分别为 0.80、0.88 和 0.74。
基于深度学习的 EV SERS 分析在诊断泌尿生殖系统癌方面具有很大的潜力，优于血清 SERS 分析，为癌症筛查提供了一种新工具。
