基于机器学习算法的胸腔积液SERS光谱和血清CEA水平中级数据融合用于肺癌的精确检测

IF 5.1 3区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Nanoscale Pub Date : 2025-06-12 DOI:10.1039/D5NR01405K
Lingna Wang, Weihua Hong, Dage Fan, Jinyong Lin, Zeyang Liu, Min Fan, Xueliang Lin, Duo Lin and Shangyuan Feng
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引用次数: 0

摘要

准确识别临床恶性胸腔积液对癌症诊断和后续治疗计划至关重要。本研究将胸腔积液的表面增强拉曼光谱(SERS)数据与血清癌胚抗原(CEA)水平进行整合,开发了一种创新的中级数据融合方法,并结合机器学习算法来提高癌症检测的准确性。采用手持式拉曼光谱仪采集了15例肺癌患者、10例其他癌症患者和28例非癌症患者胸腔积液的SERS光谱。将SERS谱的主成分分析(PCA)得分与数字化的血清CEA值合并形成数据融合阵列。采用线性判别分析(LDA)、k近邻(KNN)和支持向量机(SVM)等机器学习算法对融合数据集进行五重交叉验证训练。值得注意的是,与纯粹的SERS光谱识别模型相比,融合策略取得了更好的性能,KNN算法在区分肺癌与非癌症、其他癌症与非癌症、肺癌与其他癌症三个临床组方面表现出非常高的准确率(>85%)。这些结果突出了分子光谱指纹与肿瘤生物标志物相结合在胸腔积液分析中的协同诊断能力,从而为通过液体活检快速准确地诊断临床癌症提供了一种新的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mid-level data fusion of pleural effusion SERS spectra and serum CEA levels using machine learning algorithms for precise lung cancer detection

Mid-level data fusion of pleural effusion SERS spectra and serum CEA levels using machine learning algorithms for precise lung cancer detection

Accurate identification of clinically malignant pleural effusions is critical for cancer diagnosis and subsequent treatment planning. Here, surface-enhanced Raman spectroscopy (SERS) data of pleural effusions and serum carcinoembryonic antigen (CEA) levels were integrated to develop an innovative mid-level data fusion method combined with machine learning algorithms to improve the accuracy of cancer detection. SERS spectra of pleural effusions from 15 lung cancer patients, 10 other cancer patients, and 28 non-cancer patients were first acquired using a handheld Raman spectrometer. The principal component analysis (PCA) scores from the SERS spectra were merged with the digitized serum CEA values to generate a data fusion array. Machine learning algorithms such as linear discriminant analysis (LDA), k-nearest neighbor (KNN), and support vector machine (SVM) were applied to train the fused dataset using five-fold cross-validation. Notably, the fusion strategy achieved superior performance compared to the pure SERS spectral discrimination model, with the KNN algorithm demonstrating very high accuracy (>85%) in distinguishing the three clinical groups of lung cancer vs. non-cancer, other cancers vs. non-cancer, and lung cancer vs. other cancers. These results highlight the synergistic diagnostic capability of combining molecular spectroscopic fingerprints with tumor biomarkers for pleural effusion analysis, thereby providing a new strategy for rapid and accurate clinical cancer discrimination via liquid biopsy.

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来源期刊
Nanoscale
Nanoscale CHEMISTRY, MULTIDISCIPLINARY-NANOSCIENCE & NANOTECHNOLOGY
CiteScore
12.10
自引率
3.00%
发文量
1628
审稿时长
1.6 months
期刊介绍: Nanoscale is a high-impact international journal, publishing high-quality research across nanoscience and nanotechnology. Nanoscale publishes a full mix of research articles on experimental and theoretical work, including reviews, communications, and full papers.Highly interdisciplinary, this journal appeals to scientists, researchers and professionals interested in nanoscience and nanotechnology, quantum materials and quantum technology, including the areas of physics, chemistry, biology, medicine, materials, energy/environment, information technology, detection science, healthcare and drug discovery, and electronics.
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