Mohamed Shehata1, Ahmed Shalaby1, Andrew E Switala1, Maryam El-Baz1, Mohammed Ghazal2, Luay Fraiwan2, Ashraf Khalil3, Mohamed Abou El-Ghar4, Mohamed Badawy4, Ashraf M Bakr5, Amy Dwyer6, Adel Elmaghraby7, Guruprasad Giridharan8, Robert Keynton8, Ayman El-Baz8,9. 1. BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA. 2. Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, 59911, UAE. 3. Computer Science and Information Technology Department, Abu Dhabi University, Abu Dhabi, 59911, UAE. 4. Urology and Nephrology Center, Radiology Department, Mansoura University, Mansoura, 35516, Egypt. 5. Pediatric Nephrology Unit, Mansoura University Children's Hospital, University of Mansoura, Mansoura, 35516, Egypt. 6. Kidney Disease Program, University of Louisville, Louisville, KY, 40202, USA. 7. Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY, 40208, USA. 8. Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA. 9. 200 E Shipp Ave, Lutz 390 Hall, Room 419, Louisville, KY, 40208, USA.
Abstract
PURPOSE: Early assessment of renal allograft function post-transplantation is crucial to minimize and control allograft rejection. Biopsy - the gold standard - is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer-assisted diagnostic (Renal-CAD) system was developed to assess kidney transplant function. METHODS: The developed Renal-CAD system integrates data collected from two image-based sources and two clinical-based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion-weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm 2 ), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal-CAD system initially performed kidney segmentation using the level-set method, followed by estimation of the ADCs from DW-MRIs and the R2* from BOLD-MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning-based classifier, namely stacked autoencoders (SAEs) to differentiate non-rejection (NR) from acute rejection (AR) renal transplants. RESULTS: Using a leave-one-subject-out cross-validation approach along with SAEs, the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross-validation approach, the Renal-CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. CONCLUSION: The obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal-CAD system.
PURPOSE: Early assessment of renal allograft function post-transplantation is crucial to minimize and control allograft rejection. Biopsy - the gold standard - is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer-assisted diagnostic (Renal-CAD) system was developed to assess kidney transplant function. METHODS: The developed Renal-CAD system integrates data collected from two image-based sources and two clinical-based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion-weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm 2 ), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal-CAD system initially performed kidney segmentation using the level-set method, followed by estimation of the ADCs from DW-MRIs and the R2* from BOLD-MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning-based classifier, namely stacked autoencoders (SAEs) to differentiate non-rejection (NR) from acute rejection (AR) renal transplants. RESULTS: Using a leave-one-subject-out cross-validation approach along with SAEs, the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross-validation approach, the Renal-CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. CONCLUSION: The obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal-CAD system.
Authors: Arjang Djamali; Elizabeth A Sadowski; Millie Samaniego-Picota; Sean B Fain; Rebecca J Muehrer; Sara K Alford; Thomas M Grist; Bryan N Becker Journal: Transplantation Date: 2006-09-15 Impact factor: 4.939
Authors: Bertram L Kasiske; Martin G Zeier; Jeremy R Chapman; Jonathan C Craig; Henrik Ekberg; Catherine A Garvey; Michael D Green; Vivekanand Jha; Michelle A Josephson; Bryce A Kiberd; Henri A Kreis; Ruth A McDonald; John M Newmann; Gregorio T Obrador; Flavio G Vincenti; Michael Cheung; Amy Earley; Gowri Raman; Samuel Abariga; Martin Wagner; Ethan M Balk Journal: Kidney Int Date: 2009-10-21 Impact factor: 10.612
Authors: Lan Lu; John R Sedor; Vikas Gulani; Jeffrey R Schelling; Alicia O'Brien; Chris A Flask; Katherine MacRae Dell Journal: Am J Nephrol Date: 2011-10-18 Impact factor: 3.754
Authors: Ana Barragán-Montero; Umair Javaid; Gilmer Valdés; Dan Nguyen; Paul Desbordes; Benoit Macq; Siri Willems; Liesbeth Vandewinckele; Mats Holmström; Fredrik Löfman; Steven Michiels; Kevin Souris; Edmond Sterpin; John A Lee Journal: Phys Med Date: 2021-05-09 Impact factor: 2.685