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NON-INVASIVE DIAGNOSTICS OF LUNG CANCER BASED ON WHOLE BLOOD SURFACE-ENHANCED RAMAN SPECTROSCOPY AND DEEP MACHINE LEARNING
Abstract
Combining deep machine learning with silver nanoparticle (Ag NP)-based surface-enhanced Raman spectroscopy (SERS), we have developed a novel method for whole blood analysis for cancer detection applications. The whole blood was collected from two groups: one group of patients (n = 26) with lung cancer and another group of healthy volunteers (n = 45). The logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), and random forest (RF) algorithms were employed to develop a diagnostic model using the same spectral data. The results show that the diagnostic accuracy of LR, KNN, DT, and RF models was 87, 66, 77, and 83%, respectively. LR is superior to other algorithms in the SERS spectra classification of whole blood. We therefore believe that this proposed strategy will have great clinical potential for SERS technology combined with LR and act as a complementary method for the detection of lung cancer.
Supporting agencies: We are grateful for the support by the Fujian Provincial Department of Science and Technology guiding (Project No. 2020Y0019); Industry-university Cooperation Project of the Fujian Provincial Department of Science and Technology (No. 2020N5006); the National Natural Science Foundation of China (No. 61772023), the National Key Research and Development Program of China (No. 2019QY1803), and the Fujian Science and Technology Plan Industry-University-Research Cooperation Project (No. 2021H6015).
About the Authors
C. Chen
College of Chemistry and Materials Science, Fujian Provincial Key Laboratory of Advanced Oriented Chemical Engineer, Fujian Key Laboratory of Polymer Materials, Fujian Normal University
China
China
Cairou Chen
Fuzhou, Fujian
Q. Zhang
School of Informatics, Xiamen University
China
China
Qibin Zhang
Xiamen, Fujian Province
D. Lu
College of Chemistry and Materials Science, Fujian Provincial Key Laboratory of Advanced Oriented Chemical Engineer, Fujian Key Laboratory of Polymer Materials, Fujian Normal University
China
China
Dechan Lu
Fuzhou, Fujian
J. Liu
School of Informatics, Xiamen University
China
China
Jiatong Liu
Xiamen, Fujian Province
Y. Lu
College of Chemistry and Materials Science, Fujian Provincial Key Laboratory of Advanced Oriented Chemical Engineer, Fujian Key Laboratory of Polymer Materials, Fujian Normal University
China
China
Yudong Lu
Fuzhou, Fujian
K. Liu
School of Informatics, Xiamen University
China
China
Kunhong Liu
Xiamen, Fujian Province
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Review
For citations:
Chen C., Zhang Q., Lu D., Liu J., Lu Y., Liu K. NON-INVASIVE DIAGNOSTICS OF LUNG CANCER BASED ON WHOLE BLOOD SURFACE-ENHANCED RAMAN SPECTROSCOPY AND DEEP MACHINE LEARNING. Zhurnal Prikladnoii Spektroskopii. 2022;89(5):674-681.
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