The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence
have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play
important roles in the early diagnosis and individualized treatment. This paper presents an effective method to
distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the
fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation
(BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver
cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by
the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern
recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the
network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more
than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal
microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the
liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in
vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously
referential in practical application.
Stiffness of arteries, especially small arteries, is an important marker for many diseases and a good parameter to evaluate the risks of cardiovascular problems. In this research, we proposed a new method for measurement of local arterial distensibility by using photoacoustic microscopy (PAM) technology. Taking advantages from its excellent sensitivity and high spatial resolution, PAM can evaluate the morphology and volume change of a small artery accurately without involving any contrast agent. When working in the linear elastic range of a vessel, measuring the initial and the distended diameters of the vessel before and after pressure change facilitates quantitative assessment of vessel distensibility. The preliminary experiment on well-controlled gel phantoms demonstrates the feasibility of this technology.