A multimodal computer‐aided diagnostic system for precise identification of renal allograft rejection: Preliminary results - Shehata, Mohamed; Shalaby, Ahmed; Switala, Andrew E.; El‐Baz, Maryam; Ghazal, Mohammed; Fraiwan, Luay; Khalil, Ashraf; El‐Ghar, Mohamed Abou; Badawy, Mohamed; Bakr, Ashraf M.; Dwyer, Amy; Elmaghraby, Adel; et al. (Medical physics (Lancaster), 2020)
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/mm2), 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.