Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples.

Journal: Biosensors
PMID: 39194601

Abstract

The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological cell samples, but current FNA cytology is labor-intensive, time-consuming, and can lead to the risk of false-negative results. Surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms holds promise for cancer diagnosis. In this study, we develop a label-free SERS liquid biopsy method with machine learning for the rapid and accurate diagnosis of thyroid cancer by using thyroid FNA washout fluids. These liquid supernatants are mixed with silver nanoparticle colloids, and dispersed in quartz capillary for SERS measurements to discriminate between healthy and malignant samples. We collect Raman spectra of 36 thyroid FNA samples (18 malignant and 18 benign) and compare four classification models: Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The results show that the CNN algorithm is the most precise, with a high accuracy of 88.1%, sensitivity of 87.8%, and the area under the receiver operating characteristic curve of 0.953. Our approach is simple, convenient, and cost-effective. This study indicates that label-free SERS liquid biopsy assisted by deep learning models holds great promise for the early detection and screening of thyroid cancer.

Authors

  • Lili Gao
    Department of Pathology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Siyi Wu
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Puwasit Wongwasuratthakul
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Zhou Chen
    Department of Neurosurgery, Xiangya Hospital, Central South University, 87 Xiangya Street, Changsha, Hunan 410008, China (W.T., S.L., C.Z., Z.C., Z.H., F.C.); National Clinical Research Center for Geriatric Disorders, Central South University, 87 Xiangya Street, Changsha, Hunan 410008, China (W.T., S.L., C.Z., Z.C., Z.H., F.C.); Hypothalamic-Pituitary Research Center, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China (W.T., S.L., C.Z., Z.C., Z.H., F.C.).
  • Wei Cai
    Department of Gastrointestinal Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Qinyu Li
    Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Second Road, Shanghai 200025, China.
  • Linley Li Lin
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China. Electronic address: linli92@sjtu.edu.cn.

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