title: Investigation of gastric cancers in nude mice using X-ray in-line phase contrast imaging.
abstract: BACKGROUND
  This paper is to report the new imaging of gastric cancers without the use of imaging agents. Both gastric normal regions and gastric cancer regions can be distinguished by using the principal component analysis (PCA) based on the gray level co-occurrence matrix (GLCM).
  
  
  METHODS
  Human gastric cancer BGC823 cells were implanted into the stomachs of nude mice. Then, 3, 5, 7, 9 or 11 days after cancer cells implantation, the nude mice were sacrificed and their stomachs were removed. X-ray in-line phase contrast imaging (XILPCI), an X-ray phase contrast imaging method, has greater soft tissue contrast than traditional absorption radiography and generates higher-resolution images. The gastric specimens were imaged by an XILPCIs' charge coupled device (CCD) of 9 μm image resolution. The PCA of the projective images' region of interests (ROIs) based on GLCM were extracted to discriminate gastric normal regions and gastric cancer regions. Different stages of gastric cancers were classified by using support vector machines (SVMs).
  
  
  RESULTS
  The X-ray in-line phase contrast images of nude mice gastric specimens clearly show the gastric architectures and the details of the early gastric cancers. The phase contrast computed tomography (CT) images of nude mice gastric cancer specimens are better than the traditional absorption CT images without the use of imaging agents. The results of the PCA of the texture parameters based on GLCM of normal regions is (F1+F2) >8.5, but those of cancer regions is (F1+F2) <8.5. The classification accuracy is 83.3% that classifying gastric specimens into different stages using SVMs.
  
  
  CONCLUSIONS
  This is a very preliminary feasibility study. With further researches, XILPCI could become a noninvasive method for future the early detection of gastric cancers or medical researches.
authors: Qiang
authors: Q
authors: Tao
authors: Biomedical Engineering Institute, Capital Medical University, Beijing 100069, China. shuqian_luo@aliyun.com.
authors: Shuqian
authors: S
authors: Luo
mesh_terms: D000818:Animals; D045744:Cell Line, Tumor; D002471:Cell Transformation, Neoplastic; D006801:Humans; D051379:Mice; D008819:Mice, Nude; D025341:Principal Component Analysis; D011859:Radiography / Q000379:methods*; D013274:Stomach Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed