Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial-least-square discriminant analysis and support vector machine.

Journal: Journal of biophotonics
Published Date: Jan 28, 2019

Abstract

This study investigated the feasibility of using fluorescence hyperspectral imaging technology to diagnose of early-stage gastric cancer. Fluorescence spectral images of 76 patients who were pathologically diagnosed as non-atrophic gastritis, premalignant lesions and gastric cancer were collected. Fluorescence spectra at 100-pixel points were randomly extracted after binarization. Diagnostic models of non-atrophic gastritis, premalignant lesions and gastric cancer were constructed through partial-least-square discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms. The prediction effects of PLS-DA and SVM models were compared. Results showed that the average spectra of normal, precancerous and gastric cancer tissues significantly differed at 496, 546, 640 and 670 nm, and regular changes in fluorescence intensity at 546 nm were in the following order: normal > precancerous lesions > gastric cancer. Additionally, the effect of the diagnostic model established by SVM is significantly better than PLS-DA which accuracy, specificity and sensitivity are above 94%. Experimental results revealed that the fast diagnostic model of early gastric cancer by combining fluorescence hyperspectral imaging technology and improved SVM was effective and feasible, thereby providing an accurate and rapid method for diagnosing early-stage gastric cancer.

Authors

  • Yuanpeng Li
    Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Jinan University, Guangzhou 510632, China; Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China.
  • Xiaojuan Xie
    Department of Gastroenterology and Endocrinology, The 74th Group Army Hospital of the PLA, Guangzhou, China.
  • Xinhao Yang
    Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China.
  • Liu Guo
    Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China.
  • Zhao Liu
    Centre for Nanohealth, Swansea University Medical School, Swansea, UK.
  • Xiaoping Zhao
    Department of Gastroenterology and Endocrinology, The 74th Group Army Hospital of the PLA, Guangzhou, China.
  • Ying Luo
    School of Statistics, Beijing Normal University, Beijing, China.
  • Wei Jia
    Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
  • Furong Huang
    Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Jinan University, Guangzhou 510632, China; Research Institute of Jinan University in Dongguan, Dongguan 523000, China. Electronic address: furong_huang@jnu.edu.cn.
  • Siqi Zhu
    Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Jinan University, Guangzhou, China.
  • Zhenqiang Chen
    Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Jinan University, Guangzhou 510632, China; Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China. Electronic address: tzqchen@jnu.edu.cn.
  • Xingdan Chen
    Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Jinan University, Guangzhou 510632, China.
  • Zhong Wei
    Department of Gastroenterology, Zhujiang Hospital of the Southern Medical University, Guangzhou, China.
  • Weimin Zhang
    School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China.

Keywords

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