Investigation of gastric cancers in nude mice using X-ray in-line phase contrast imaging

BioMedical Engineering OnLine

Qiang Tao¹  Shuqian Luo¹ 

[1] Biomedical Engineering Institute, Capital Medical University, Beijing 100069, China

关键词: Support vector machine;    Principal component analysis;    Gastric cancer;    X-ray absorption imaging;    X-ray in-line phase contrast imaging;   

Others  :  1084670
DOI  :  10.1186/1475-925X-13-101

 received in 2014-05-27, accepted in 2014-07-21,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Tao and Luo; licensee BioMed Central Ltd.

【 图 表 】

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