Correlation between Kidney Function and Sonographic Texture Features after Allograft Transplantation with Corresponding to Serum Creatinine: A Long Term Follow-Up Study

Abbasian Ardakani A; Sattar A R; Abolghasemi J; Mohammadi A

doi:10.31661/jbpe.v0i0.928

Correlation between Kidney Function and Sonographic Texture Features after Allograft Transplantation with Corresponding to Serum Creatinine: A Long Term Follow-Up Study

J Biomed Phys Eng. 2020 Dec 1;10(6):713-726. doi: 10.31661/jbpe.v0i0.928. eCollection 2020 Dec.

Authors

Abbasian Ardakani A¹, Sattar A R², Abolghasemi J³, Mohammadi A²

Affiliations

¹ PhD, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
² MD, Department of Vascular and Interventional Radiology, School of Medicine, Namazi Hospital, Shiraz University of Medical Sciences, Shiraz, Iran.
³ PhD, Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.

Abstract

Background: The ability to monitor kidney function after transplantation is one of the major factors to improve care of patients.

Objective: Authors recommend a computerized texture analysis using run-length matrix features for detection of changes in kidney tissue after allograft in ultrasound imaging.

Material and methods: A total of 40 kidney allograft recipients (28 male, 12 female) were used in this longitudinal study. Of the 40 patients, 23 and 17 patients showed increased serum creatinine (sCr) (increased group) and decreased sCr (decreased group), respectively. Twenty run-length matrix features were used for texture analysis in three normalizations. Correlations of texture features with serum creatinine (sCr) level and differences between before and after follow-up for each group were analyzed. An area under the receiver operating characteristic curve (Az) was measured to evaluate potential of proposed method.

Results: The features under default and 3sigma normalization schemes via linear discriminant analysis (LDA) showed high performance in classifying decreased group with an A_z of 1. In classification of the increased group, the best performance gains were determined in the 3sigma normalization schemes via LDA with an A_z of 0.974 corresponding to 95.65% sensitivity, 91.30% specificity, 93.47% accuracy, 91.67% PPV, and 95.45% NPV.

Conclusion: Run-length matrix features not only have high potential for characterization but also can help physicians to diagnose kidney failure after transplantation.

Keywords: Computer-Assisted; Decision Making; Kidney Transplantation; Pattern Recognition System; Ultrasonography.