title: [Application of support vector machine-recursive feature elimination algorithm in Raman spectroscopy for differential diagnosis of benign and malignant breast diseases].
abstract: OBJECTIVE
  To explore the value of application of support vector machine-recursive feature elimination (SVM-RFE) method in Raman spectroscopy for differential diagnosis of benign and malignant breast diseases.
  
  
  METHODS
  Fresh breast tissue samples of 168 patients (all female; ages 22-75) were obtained by routine surgical resection from May 2011 to May 2012 at the Department of Breast Surgery, the First Hospital of Jilin University. Among them, there were 51 normal tissues, 66 benign and 51 malignant breast lesions. All the specimens were assessed by Raman spectroscopy, and the SVM-RFE algorithm was used to process the data and build the mathematical model. Mahalanobis distance and spectral residuals were used as discriminating criteria to evaluate this data-processing method.
  
  
  RESULTS
  1 800 Raman spectra were acquired from the fresh samples of human breast tissues. Based on spectral profiles, the presence of 1 078, 1 267, 1 301, 1 437, 1 653, and 1 743 cm(-1) peaks were identified in the normal tissues; and 1 281, 1 341, 1 381, 1 417, 1 465, 1 530, and 1 637 cm(-1) peaks were found in the benign and malignant tissues. The main characteristic peaks differentiating benign and malignant lesions were 1 340 and 1 480 cm(-1). The accuracy of SVM-RFE in discriminating normal and malignant lesions was 100.0%, while that in the assessment of benign lesions was 93.0%.
  
  
  CONCLUSIONS
  There are distinct differences among the Raman spectra of normal, benign and malignant breast tissues, and SVM-RFE method can be used to build differentiation model of breast lesions.
authors.affiliation: Department of Obstetrics Surgery, the First Hospital, Jilin University, Changchun 130021, China.
authors.forename: Haipeng
authors.initials: H
authors.lastname: Zhang
authors.forename: Tong
authors.initials: T
authors.lastname: Fu
authors.forename: Zhiru
authors.initials: Z
authors.lastname: Zhang
authors.forename: Zhimin
authors.initials: Z
authors.lastname: Fan
authors.forename: Chao
authors.initials: C
authors.lastname: Zheng
authors.affiliation: Email: yintian77@126.com.
authors.forename: Bing
authors.initials: B
authors.lastname: Han
mesh_terms: D000465:Algorithms; D001941:Breast Diseases / Q000175:diagnosis*; D001943:Breast Neoplasms / Q000175:diagnosis*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D013059:Spectrum Analysis, Raman; D060388:Support Vector Machine