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

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 imag...

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Detalles Bibliográficos
Autores principales: A., Abbasian Ardakani, A. R., Sattar, J., Abolghasemi, A., Mohammadi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Shiraz University of Medical Sciences 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7753263/
https://www.ncbi.nlm.nih.gov/pubmed/33364209
http://dx.doi.org/10.31661/jbpe.v0i0.928
Descripción
Sumario: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.