ORIGINAL PAPER
Scintigraphic texture analysis for assessment of renal allograft function
Publication date: 2018-01-18
Pol J Radiol, 2018; 83: 1-10
KEYWORDS
ABSTRACT
Purpose:
Early detection and monitoring of kidney function during the post-transplantation period is one of the most important issues for improving the accuracy of an initial diagnosis. The aim of this study was to evaluate texture analysis (TA) in scintigraphic imaging to detect changes in kidney status after transplantation.
Material and methods:
Scintigraphic images were used for TA from a total of 94 kidney allografts (39 rejected and 55 non-rejected). Images corresponding to the frames at the 2nd, 5th, and 20th minute of the study were used to determine the optimum time point for analysis of differences in texture features between the rejected and non-rejected allografts.
Results:
Linear discriminant analysis indicated the best performance at the fifth minute frame for classification of the rejected and non-rejected allografts with receiver operating characteristic curve (Az) of 0.982, corresponding to 91.89% sensitivity, 96.49% specificity, and 94.68% accuracy. Also, TA can differentiate acute tubular necrosis from acute rejection with Az of 0.953 corresponding to 88% sensitivity, 92.31% specificity, and 90.62% accuracy at the 5th minute frame. The best correlation between texture feature and kidney function was achieved at the 20th minute frame (r = –0.396) for glomerular filtration rate.
Conclusions:
TA has good potential for the characterisation of kidney failure after transplantation and can improve clinical diagnosis.
Early detection and monitoring of kidney function during the post-transplantation period is one of the most important issues for improving the accuracy of an initial diagnosis. The aim of this study was to evaluate texture analysis (TA) in scintigraphic imaging to detect changes in kidney status after transplantation.
Material and methods:
Scintigraphic images were used for TA from a total of 94 kidney allografts (39 rejected and 55 non-rejected). Images corresponding to the frames at the 2nd, 5th, and 20th minute of the study were used to determine the optimum time point for analysis of differences in texture features between the rejected and non-rejected allografts.
Results:
Linear discriminant analysis indicated the best performance at the fifth minute frame for classification of the rejected and non-rejected allografts with receiver operating characteristic curve (Az) of 0.982, corresponding to 91.89% sensitivity, 96.49% specificity, and 94.68% accuracy. Also, TA can differentiate acute tubular necrosis from acute rejection with Az of 0.953 corresponding to 88% sensitivity, 92.31% specificity, and 90.62% accuracy at the 5th minute frame. The best correlation between texture feature and kidney function was achieved at the 20th minute frame (r = –0.396) for glomerular filtration rate.
Conclusions:
TA has good potential for the characterisation of kidney failure after transplantation and can improve clinical diagnosis.
REFERENCES (39)
1.
National Chronic Kidney Disease Fact Sheet: general information and national estimates on chronic kidney disease in the United States, 2010.
2.
Matas AJ, Smith JM, Skeans MA et al. OPTN/SRTR 2011 Annual Data Report: Kidney. American Journal of Transplantation 2013; 13: 11-46.
3.
Aktaş A. Transplanted kidney function evaluation. Semin Nucl Med 2014; 44: 129-145.
4.
Dimitroulis D, Bokos J, Zavos G et al. Vascular complications in renal transplantation: a single-center experience in 1367 renal transplantations and review of the literature. Transplant Proc 2009; 41: 1609-1614.
5.
Danovitch GM (ed.). Handbook of Kidney Transplantation. Wolters Kluwer Health 2010.
6.
Grenier N, Merville P, Combe C. Radiologic imaging of the renal parenchyma structure and function. Nat Rev Nephrol 2016; 12: 348-359.
7.
Abbasian Ardakani A, Mohammadi A, Khalili Najafabad B, Abolghasemi, J. Assessment of Kidney Function After Allograft Transplantation by Texture Analysis. Iran J Kidney Dis 2017; 11: 157-164.
8.
Materka A. Texture analysis methodologies for magnetic resonance imaging. Dialogues Clin Neurosci 2004; 6: 243-250.
9.
Castellano G, Bonilha L, Li LM, Cendes, F. Texture analysis of medical images. Clin Radiol 2004; 59: 1061-1069.
10.
Ardakani AA, Gharbali A, Saniei Y et al. Application of Texture Analysis in Diagnosis of Multiple Sclerosis by Magnetic Resonance Imaging. Global J Health Sci 2015; 7: 68-78.
11.
Bou Matar R, Warshaw B, Hymes L et al. Routine transplant Doppler ultrasonography following pediatric kidney transplant. Pediatr Transplant 2012; 16: 607-612.
12.
Cano H, Castańeda DA, Patińo N et al. Resistance index measured by Doppler ultrasound as a predictor of graft function after kidney transplantation. Transplant Proc 2014; 46: 2972-2974.
13.
Patel K, Patel N, Gandhi S. Comparison between doppler ultrasound resistive index, serum creatinine, and histopathologic changes in patients with kidney transplant dysfunction in early posttransplantation period: A single center study with review of literature. Saudi J Kidney Dis Transpl 2016; 27: 533-538.
14.
Yazici B, Yazici A, Oral A et al. Comparison of renal transplant scintigraphy with renal resistance index for prediction of early graft dysfunction and evaluation of acute tubular necrosis and acute rejection. Clin Nucl Med 2013; 38: 931-935.
15.
Lee J, Oh YT, Joo DJ et al. Acoustic Radiation Force Impulse Measurement in Renal Transplantation: A Prospective, Longitudinal Study With Protocol Biopsies. Medicine (Baltimore) 2015; 94: e1590.
16.
Gao J, Rubin JM, Weitzel W et al. Comparison of Ultrasound Corticomedullary Strain with Doppler Parameters in Assessment of Renal Allograft Interstitial Fibrosis/Tubular Atrophy. Ultrasound Med Biol 2015; 41: 2631-2639.
17.
Yazici B, Oral A, Gokalp C et al. Evaluation of renal transplant scintigraphy and resistance index performed within 2 days after transplantation in predicting long-term graft function. Clin Nucl Med 2015; 40: 548-552.
18.
Yoon Y-C, Shin BS, Ohm JY et al. Comparison between Doppler Ultrasonography and Renal Scintigraphy in Assessment of Post-Transplant Renal Function. J Korean Soc Radiol 2016; 74: 313-321.
19.
Gaillard F, Pavlov P, Tissier A-M et al. Use of computed tomography assessed kidney length to predict split renal GFR in living kidney donors. Eur Radiol 2017; 27: 651-659.
20.
Barbas AS, Li Y, Zair M et al. CT volumetry is superior to nuclear renography for prediction of residual kidney function in living donors. Clin Transplant 2016; 30: 1028-1035.
21.
Yanishi M, Kinoshita H, Yoshida T et al. Comparison of renal scintigraphy and computed tomographic renal volumetry for determining split renal function and estimating post-transplant renal function. Transplant Proc 2015; 47: 2700-2702.
22.
Patankar K, Low R, Blakeway D et al. Comparison of computer tomographic volumetry versus nuclear split renal function to determine residual renal function after living kidney donation. Acta Radiol 2014; 55: 753-760.
23.
Jadoul A, Lovinfosse P, Weekers L et al. The Uptake of 18F-FDG by renal allograft in kidney transplant recipients is not influenced by renal function. Clin Nucl Med 2016; 41: 683-687.
24.
Lovinfosse P, Weekers L, Bonvoisin C et al. Fluorodeoxyglucose F18 Positron Emission Tomography Coupled With Computed Tomography in Suspected Acute Renal Allograft Rejection. Am J Transplant 2016; 16: 310-316.
25.
Eikefjord E, Andersen E, Hodneland E et al. Quantification of Single-Kidney Function and Volume in Living Kidney Donors Using Dynamic Contrast-Enhanced MRI. AJR Am J Roentgenol 2016; 207: 1022-1030.
26.
Palmucci S, Cappello G, Attinà G et al. Diffusion weighted imaging and diffusion tensor imaging in the evaluation of transplanted kidneys. Eur J Radiol 2015; 2: 71-80.
27.
Ren T, Wen CL, Chen LH et al. Evaluation of renal allografts function early after transplantation using intravoxel incoherent motion and arterial spin labeling MRI. Magn Reson Imaging 2016; 34: 908-914.
28.
Hueper K, Khalifa A, Bräsen J et al. Diffusion-weighted imaging and diffusion tensor imaging detect delayed graft function and correlate with allograft fibrosis in patients early after kidney transplantation. J Magn Reson Imaging 2016; 44: 112-121.
29.
ACR-SPR PRACTICE PARAMETER FOR THE PERFORMANCE OF RENAL SCINTIGRAPHY. Available at: https://www.acr.org/Quality-Sa....
30.
Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976; 16: 31-41.
31.
Van Erkel AR, Pattynama PMT. Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. Eur J Radiol 1998; 27: 88-94.
32.
Kee TY, Chapman JR, O’Connell PJ et al. Treatment of subclinical rejection diagnosed by protocol biopsy of kidney transplants. Transplantation 2006; 82: 36-42.
33.
Sharfuddin A. Imaging evaluation of kidney transplant recipients. Semin Nephrol 2011; 31: 259-271.
34.
Aktas A, Karakayali H, Bilgin N et al. Serial radionuclide imaging in acute renal allograft dysfunction. Transplant Proc 2002; 34: 2102-2105.
35.
Gupta S, Lewis G, Rogers K et al. Quantitative 99mTc DTPA renal transplant scintigraphic parameters: assessment of interobserver agreement and correlation with graft pathologies. Am J Nucl Med Mol Imaging 2014; 4: 213-224.
36.
van Acker BAC, Koopman MG, Arisz L et al. Creatinine clearance during cimetidine administration for measurement of glomerular filtration rate. Lancet 1992; 340: 1326-1329.
37.
Andreev E, Koopman M, Arisz L. A rise in plasma creatinine that is not a sign of renal failure: which drugs can be responsible? J Intern Med 1999; 246: 247-252.
38.
Randers E, Erlandsen EJ. Serum cystatin C as an endogenous marker of the renal function – a review. Clin Chem Lab Med 1999; 37: 389-395.
39.
Gökkuşu CA, Özden TA, Gül H et al. Relationship between plasma Cystatin C and creatinine in chronic renal diseases and Tx-transplant patients. Clin Biochem 2004; 37: 94-97.
Share
RELATED ARTICLE
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
