Title of article :
Assessment of Kidney Function After Allograft Transplantation by Texture Analysis
Author/Authors :
Abbasian Ardakani, Ali Department of Medical Physics - School of Medicine - Iran University of Medical Sciences, Tehran, Iran , Mohammadi, Afshin Department of Radiology, School of Medicine - Imam Khomeini Hospital - Urmia University of Medical Science, Urmia, Iran , Khalili Najafabad, Bahareh Department of Medical Physics - School of Medicine - Iran University of Medical Sciences, Tehran, Iran , Abolghasemi, Jamileh Department of Biostatistics - School of public health - Iran University Medical of Sciences, Tehran, Iran
Abstract :
Introduction. Ultrasonography is the preferable imaging technique
for monitoring and assessing complications in kidney allograft
transplants. Computer-aided diagnostic system based on texture
analysis in ultrasonographic imaging is recommended to identify
changes in kidney function after allograft transplantation.
Materials and Methods. A total of 61 biopsy-proven kidney
allograft recipients (11 rejected and 50 unrejected) were assessed
by a computer-aided diagnostic system. Up to 270 statistical texture
features were extracted as descriptors for each region of interest
in each recipient. Correlations of texture features with serum
creatinine level and differences between rejected and unrejected
allografts were analyzed. An area under the receiver operating
characteristic curve was calculated for each significant texture
feature. Linear discriminant analysis was employed to analyze
significant features and increase discriminative power. Recipients
were classified by the first nearest neighbor classifier.
Results. Fourteen texture features had a significant correlation with
serum creatinine level and 16 were significantly different between
the rejected and unrejected allografts, for which an area under
the curve values were in the range of 0.575 for difference entropy
S(4,0) to 0.676 for kurtosis. Using all 16 features, linear discriminant
analysis indicated higher performance for classification of the two
groups with an area under the curve of 0.975, which corresponded
to a sensitivity of 90.9%, a specificity of 100%, a positive predictive
value of 100%, and a negative predictive value of 98.0%.
Conclusions. Texture analysis was a reliable method, with the
potential for characterization, and can help physicians to diagnose
kidney failure after transplantation on ultrasonographic imaging.
Keywords :
ultrasonography , pattern recognition system , kidney transplantation , computeraided diagnostics
Journal title :
Iranian Journal of Kidney Diseases (IJKD)