期刊
PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY
卷 40, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.pdpdt.2022.103115
关键词
Raman spectrum; Breast cancer; Serum; Classification
类别
资金
- Special Project of Tianshan Inno- vation Team in Xinjiang Uygur Autonomous Region
- Tianshan Youth Project in Xinjiang Uygur Autonomous Region
- Distinguished Young Talents Project of Natural Sci- ence Foundation of Xinjiang Uygur Autonomous Region
- [2020D14031]
- [2019Q043]
- [2022D01E11]
This study utilized serum Raman spectroscopy combined with multiple classification algorithms to develop an auxiliary diagnosis method for breast cancer, and the reliability of this method was validated through a large sample experiment.
Breast cancer is a malignant tumor with the highest incidence rate in women. Current diagnostic methods are time-consuming, costly, and dependent on physician experience. In this study, we used serum Raman spec-troscopy combined with multiple classification algorithms to implement an auxiliary diagnosis method for breast cancer, which will help in the early diagnosis of breast cancer patients. We analyzed the serum Raman spectra of 171 invasive ductal carcinoma (IDC) and 100 healthy volunteers; The analysis showed differences in nucleic acids, carotenoids, amino acids, and lipid concentrations in their blood. These differences provide a theoretical basis for this experiment. First, we used adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky-Golay (SG) for baseline correction and smoothing denoising to remove the effect of noise on the experiment. Then, the Principal component analysis (PCA) algorithm was used to extract features. Finally, we built four classification models: support vector machine (SVM), decision tree (DT), linear discriminant analysis (LDA), and Neural Network Language Model (NNLM). The LDA, SVM, and NNLM achieve 100% accuracy. As supplementary, we added the classification experiment of the raw data. By comparing the experimental results of the two groups, We concluded that the NNLM was the best model. The results show the reliability of the com-bination of serum Raman spectroscopy and classification models under large sample conditions.
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