journal article·2025-01-22Open access

Discrimination of Benign and Malignant Thyroid Nodules through Comparative Analyses of Human Saliva Samples via Metabolomics and Deep-Learning-Guided Label-free SERS

Jia-Wei Tang, Jing-Yi Mou, Jie Chen, Quan Yuan, Xin-Ru Wen, Qing-Hua Liu, Zhao Liu, Liang Wang

Abstract

Thyroid nodules are a very common entity. The overall prevalence in the populace is estimated to be around 65–68%, among which a small portion (less than 5%) is malignant (cancerous). Therefore, it is important to discriminate benign thyroid nodules from malignant thyroid nodules. In this study, an equal number of participants with benign and malignant thyroid nodules ( N = 10/group) were recruited. Saliva samples were collected from each participant, and SERS spectra were acquired, followed by validation using a metabolomics approach. An additional equal number of patients ( N = 40/group) were recruited to construct diagnostic models. The performance of various machine learning (ML) algorithms was assessed using multiple evaluation metrics. Finally, the reliability of the optimal model was tested using blind test data ( N = 10/group for benign and malignant thyroid nodules). The results showed a consistent trend between the SERS metabolic profile and the metabolites identified through MS analysis. The Multi-ResNet algorithm was optimal, achieving a 95% accuracy in sample discrimination. Additionally, blind test data sets yielded an overall accuracy of 83%. In summary, the deep-learning-guided SERS technique holds great potential in the accurate discrimination of benign and malignant thyroid nodules via human saliva samples, which facilitates the noninvasive diagnosis of malignant thyroid nodules in clinical settings.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Identification and Quantification in Food · Healthcare and Venom Research · Metabolomics and Mass Spectrometry Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province 510080, China

Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China

Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province 210000, China

State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macao Special Administrative Region of China 999078, China

Department of Thyroid and Breast Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province 210000, China

Department of Clinical Medicine, School of first Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu Province 210000, China

Division of Microbiology and Immunology, School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia 6009, Australia

The Center for Precision Health, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia 6027, Australia

Funding

Research Foundation for Advanced Talents of Guandong Provincial People?s Hospital

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