Diagnosis of cervical squamous cell carcinoma and cervical adenocarcinoma based on Raman spectroscopy and support vector machine.

Journal: Photodiagnosis and photodynamic therapy
Published Date:

Abstract

In this report, we collected the Raman spectrum of cervical adenocarcinoma and cervical squamous cell carcinoma tissues by a micro-Raman spectroscopy system. We analysed, compared and summarized the characteristics and differences of the normalized mean Raman spectra of the two tissues and pointed out the major differences in the biochemical composition between the two tissues. The PCA-SVM model that was used to distinguish the two types of cervical cancer tissues was established. The accuracy of the model in differentiating cervical adenocarcinoma from cervical squamous cell carcinoma was 93.125%. The results of this study indicate that Raman spectroscopy of cervical adenocarcinoma and cervical squamous cell carcinoma tissue in combination with SVM (support vector analysis) and PCA (principal component analysis) can be useful for the classification of cervical adenocarcinoma and cervical squamous cell carcinoma tissues and for the exploration of the differences in biochemical compositions between the two types of cervical tissue. This study lays a foundation to further study Raman spectroscopy as a clinical diagnostic method for cervical cancer.

Authors

  • Chengxia Zheng
    College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Changji Vocational and Technical College, Changji City 831100, Xinjiang Uygur Autonomous Region, China.
  • Song Qing
    Pathology Department of The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Guodong Lü
    State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China.
  • Hongyi Li
    State Key Laboratory of Robotics, Shenyang Institute of Automation, University of Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China.
  • Xiaoyi Lü
    College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China. Electronic address: xiaoz813@163.com.
  • Cailing Ma
    Gynecology Department of The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China. Electronic address: hymcl13009661999@126.com.
  • Jun Tang
    School of Electronics and Information Engineering, Anhui University, Hefei, China.
  • Xiaxia Yue
    Physics and Chemistry Detecting Center, Xinjiang University, Urumqi 830046, China.