title: Intraoperative diagnosis of benign and malignant breast tissues by fourier transform infrared spectroscopy and support vector machine classification.
abstract: BACKGROUND
  Fourier transform infrared (FTIR) spectroscopy has shown its unique advantages in distinguishing cancerous tissue from normal one. The aim of this study was to establish a quick and accurate diagnostic method of FTIR spectroscopy to differentiate malignancies from benign breast tissues intraoperatively.
  
  
  METHODS
  In this study, a total of 100 breast tissue samples obtained from 100 patients were taken on surgery. All tissue samples were scanned for spectra intraoperatively before being processed for histopathological diagnosis. Standard normal variate (SNV) method was adopted to reduce scatter effects. Support vector machine (SVM) classification was used to discriminate spectra between malignant and benign breast tissues. Leave-one-out cross validation (LOOCV) was used to evaluate the discrimination.
  
  
  RESULTS
  According to histopathological examination, 50 cases were diagnosed as fibroadenoma and 50 cases as invasive ductal carcinoma. The results of SVM algorithm showed that the sensitivity, specificity and accuracy rate of this method are 90.0%, 98.0% and 94.0%, respectively.
  
  
  CONCLUSIONS
  FTIR spectroscopy technique in combination with SVM classification could be an accurate, rapid and objective tool to differentiate malignant from benign tumors during operation. Our studies establish the feasibility of FTIR spectroscopy with chemometrics method to guide surgeons during the surgery as an effective supplement for pathological diagnosis on frozen section.
authors.affiliation: Department of General Surgery, Peking University Third Hospital Beijing, China.
authors.forename: Peirong
authors.initials: P
authors.lastname: Tian
authors.affiliation: Department of General Surgery, Peking University Third Hospital Beijing, China.
authors.forename: Weitao
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Department of General Surgery, Peking University Third Hospital Beijing, China.
authors.forename: Hongmei
authors.initials: H
authors.lastname: Zhao
authors.affiliation: Department of General Surgery, Peking University Third Hospital Beijing, China.
authors.forename: Yutao
authors.initials: Y
authors.lastname: Lei
authors.affiliation: Department of General Surgery, Peking University Third Hospital Beijing, China.
authors.forename: Long
authors.initials: L
authors.lastname: Cui
authors.affiliation: School of Instrumentation Science and Opto-Electronics Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University Beijing, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: School of Instrumentation Science and Opto-Electronics Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University Beijing, China.
authors.forename: Qingbo
authors.initials: Q
authors.lastname: Li
authors.affiliation: College of Chemistry and Molecular Engineering, Peking University Beijing, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Zhu
authors.affiliation: College of Chemistry and Molecular Engineering, Peking University Beijing, China.
authors.forename: Yuanfu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of General Surgery, Peking University Third Hospital Beijing, China.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Xu
keywords: FTIR spectroscopy; breast tumor; intraoperative diagnosis; support vector machine