Next Article in Journal
Fire up Biosensor Technology to Assess the Vitality of Trees after Wildfires
Previous Article in Journal
Brillouin Biosensing of Viscoelasticity across Phase Transitions in Ovine Cornea
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biosensors
Volume 14
Issue 8
10.3390/bios14080372
biosensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Lili Gao
1,
Siyi Wu
2,
Puwasit Wongwasuratthakul
2,
Zhou Chen
2,
Wei Cai
3,
Qinyu Li
3,* and
Linley Li Lin
2
1
Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Second Road, Shanghai 200025, China
2
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
3
Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Second Road, Shanghai 200025, China
*
Author to whom correspondence should be addressed.
Biosensors 2024, 14(8), 372; https://doi.org/10.3390/bios14080372
Submission received: 13 June 2024 / Revised: 25 July 2024 / Accepted: 26 July 2024 / Published: 31 July 2024
(This article belongs to the Special Issue Emerging Surface-Enhanced Raman Scattering Strategies and Applications for Biosensors)
Browse Figures
Versions Notes

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.
Keywords: Raman spectroscopy; thyroid fluid; machine learning; CNN; liquid biopsy Raman spectroscopy; thyroid fluid; machine learning; CNN; liquid biopsy

Share and Cite

MDPI and ACS Style

Gao, L.; Wu, S.; Wongwasuratthakul, P.; Chen, Z.; Cai, W.; Li, Q.; Lin, L.L. Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples. Biosensors 2024, 14, 372. https://doi.org/10.3390/bios14080372

AMA Style

Gao L, Wu S, Wongwasuratthakul P, Chen Z, Cai W, Li Q, Lin LL. Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples. Biosensors. 2024; 14(8):372. https://doi.org/10.3390/bios14080372

Chicago/Turabian Style

Gao, Lili, Siyi Wu, Puwasit Wongwasuratthakul, Zhou Chen, Wei Cai, Qinyu Li, and Linley Li Lin. 2024. "Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples" Biosensors 14, no. 8: 372. https://doi.org/10.3390/bios14080372

APA Style

Gao, L., Wu, S., Wongwasuratthakul, P., Chen, Z., Cai, W., Li, Q., & Lin, L. L. (2024). Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples. Biosensors, 14(8), 372. https://doi.org/10.3390/bios14080372

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop