Mid-level data fusion strategy based on urinary nucleosides SERS spectra and blood CEA levels for enhanced preoperative detection of lymph node metastasis in colorectal cancer

IF 5.7 2区化学 Q1 CHEMISTRY, ANALYTICAL

Analytica Chimica Acta Pub Date : 2024-10-22 DOI:10.1016/j.aca.2024.343360

Jinyong Lin , Yuduo Wu , Zhizhong Lin , Xueliang Lin , Qiong Wu , JiaJia Lin , Yuanji Xu , Shangyuan Feng , Junxin Wu

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引用次数: 0

Abstract

Background

Preoperative prediction of lymph node metastasis (LNM) plays a crucial role in the treatment and prognosis of colorectal cancer (CRC). The traditional histopathological examination is invasive and time-consuming, providing pathological features only postoperatively. Preoperative serum carcinoembryonic antigen (CEA) is strongly correlated with postoperative LN status. However, the detection accuracy of LNM based on a single preoperative CEA level is low. Therefore, developing a more powerful and sensitive diagnostic tool would be of great clinical value for improving the accurate preoperative prediction of LNM in CRC patients.

Results

This study aimed to develop a mid-level fusion approach using urinary nucleosides Raman spectra and blood CEA data to enhance the preoperative discrimination of CRC patients with and without LNM. Surface-enhanced Raman scattering (SERS) spectra of urinary modified nucleosides, isolated by affinity chromatography, were first acquired from 48 patients with LNM and 49 patients without LNM. The principal component analysis (PCA) scores obtained from the SERS spectra were then combined with preoperative blood CEA values to create a fused data array. The discriminant accuracy based on either dataset alone or the fused data was evaluated using three machine learning algorithms: linear discriminant analysis, k-nearest neighbors, and support vector machine. Results showed that the fused data could discriminate between the two groups with an accuracy of up to 91 %, outperforming SERS alone (86 %) and CEA alone (69 %).

Significance

To our knowledge, this is the first report of mid-level data fusion of urinary nucleosides SERS spectra with blood CEA levels for the preoperative prediction of LNM in CRC. This work demonstrates that the mid-level data fusion strategy aided by SVM algorithm can greatly improve the preoperative prediction accuracy of LNM. This is crucial for therapeutic decision-making and prognostic assessment in CRC.

Abstract Image

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基于尿核苷 SERS 光谱和血 CEA 水平的中层数据融合策略用于增强结直肠癌淋巴结转移的术前检测

背景术前预测淋巴结转移（LNM）对结直肠癌（CRC）的治疗和预后起着至关重要的作用。传统的组织病理学检查具有创伤性且耗时，只能在术后提供病理特征。术前血清癌胚抗原（CEA）与术后 LN 状态密切相关。然而，基于单一术前 CEA 水平的 LNM 检测准确率很低。因此，开发一种更强大、更灵敏的诊断工具将对提高 CRC 患者术前 LNM 的准确预测具有重要的临床价值。结果本研究旨在开发一种中层融合方法，利用尿液核苷拉曼光谱和血液 CEA 数据来提高有 LNM 和无 LNM 的 CRC 患者的术前分辨能力。首先从 48 名 LNM 患者和 49 名非 LNM 患者中获取了通过亲和层析法分离的尿液修饰核苷的表面增强拉曼散射（SERS）光谱。然后将从 SERS 光谱中获得的主成分分析 (PCA) 分数与术前血液 CEA 值相结合，创建一个融合数据阵列。使用三种机器学习算法（线性判别分析、k-近邻和支持向量机）评估了基于单独数据集或融合数据的判别准确性。结果表明，融合数据对两组患者的区分准确率高达 91%，优于单独使用 SERS（86%）和单独使用 CEA（69%）。这项工作表明，SVM 算法辅助下的中层数据融合策略可大大提高 LNM 的术前预测准确率。这对 CRC 的治疗决策和预后评估至关重要。

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来源期刊

Analytica Chimica Acta 化学-分析化学

CiteScore

10.40

自引率

6.50%

发文量

1081

审稿时长

38 days

期刊介绍： Analytica Chimica Acta has an open access mirror journal Analytica Chimica Acta: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review. Analytica Chimica Acta provides a forum for the rapid publication of original research, and critical, comprehensive reviews dealing with all aspects of fundamental and applied modern analytical chemistry. The journal welcomes the submission of research papers which report studies concerning the development of new and significant analytical methodologies. In determining the suitability of submitted articles for publication, particular scrutiny will be placed on the degree of novelty and impact of the research and the extent to which it adds to the existing body of knowledge in analytical chemistry.