Lingna Wang, Weihua Hong, Dage Fan, Jinyong Lin, Zeyang Liu, Min Fan, Xueliang Lin, Duo Lin and Shangyuan Feng
{"title":"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","authors":"Lingna Wang, Weihua Hong, Dage Fan, Jinyong Lin, Zeyang Liu, Min Fan, Xueliang Lin, Duo Lin and Shangyuan Feng","doi":"10.1039/D5NR01405K","DOIUrl":null,"url":null,"abstract":"<p >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), <em>k</em>-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 <em>vs.</em> non-cancer, other cancers <em>vs.</em> non-cancer, and lung cancer <em>vs.</em> 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 <em>via</em> liquid biopsy.</p>","PeriodicalId":92,"journal":{"name":"Nanoscale","volume":" 27","pages":" 16349-16360"},"PeriodicalIF":5.1000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mid-level data fusion of pleural effusion SERS spectra and serum CEA levels using machine learning algorithms for precise lung cancer detection\",\"authors\":\"Lingna Wang, Weihua Hong, Dage Fan, Jinyong Lin, Zeyang Liu, Min Fan, Xueliang Lin, Duo Lin and Shangyuan Feng\",\"doi\":\"10.1039/D5NR01405K\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >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), <em>k</em>-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 <em>vs.</em> non-cancer, other cancers <em>vs.</em> non-cancer, and lung cancer <em>vs.</em> 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 <em>via</em> liquid biopsy.</p>\",\"PeriodicalId\":92,\"journal\":{\"name\":\"Nanoscale\",\"volume\":\" 27\",\"pages\":\" 16349-16360\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2025-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nanoscale\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://pubs.rsc.org/en/content/articlelanding/2025/nr/d5nr01405k\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nanoscale","FirstCategoryId":"88","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/nr/d5nr01405k","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
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.