Thursday, November 21st, 2019

 

Next
A full text version of this article is available.
To access article obtain online access here or login
 
Title:
Evaluating the Diagnostic Efficiency of Computerized Image Analysis Based on Quantitative Nuclear Parameters in Different Grades of Hepatocellular Carcinoma
Authors:  Rowaida Saadawi, M.Phil., Jiexia Guan, M.Phil., Zhenning Zou, Ph.D., and Hong Shen, Ph.D., M.D.
  Objective: To evaluate the ability of computer-assisted quantitative methods using morphometric imaging software to differentiate between hematoxylin and eosin–stained nuclei of different pathological grades of hepatocellular carcinoma (HCC).
Study Design:
Computerized morphometric features of cell nuclei in paraffin-embedded histological sections of HCC were analyzed using Image-Pro Plus 6 software. Morphometric analysis was performed using an optical microscope and micro camera. Seventy-five cases of HCC with different histological grades (I, II, and III) were collected from 75 slides. Nuclear imaging analysis was performed to measure different morphometric variables in each sample by computer image analysis software. An average of 10 fields of vision were systematically chosen under the microscope, and a minimum of 150 nuclei were analyzed from each imaging field. The correlation between the pathological grading and the examined parameter was statistically analyzed.
Results:
The nuclear morphometric parameters (area, major axis, minor axis, and perimeter) of tumor cells were significantly increased in HCC with higher histological grading (p<0.05). There was significant difference in the density number and nuclear area of tumor cells between the low- and high-grade HCC (p<0.05). Interestingly, the nuclear to cytoplasmic ratios were in- creased with grading degree of the HCC.
Conclusion:
Computer-assisted imaging analysis of nuclear morphometric and densitometric features of HCC cells are important diagnostic parameters for histological grading of tumors and might help for significant diagnosis of HCC.
Keywords:  analysis, computer-assisted image; computer-assisted image analysis; computer-assisted image processing; diagnostic imaging; hepatocellular carcinoma; hepatoma; image analysis, computer-assisted; image processing, computer-assisted; image reconstruction; liver cancer, adult; liver cell carcinoma; morphometry, nuclear area; tumor grading
   
   
  Acrobat Reader 7.0 is recommended to properly view and print the article.
Reader can be downloaded from