Shehata Mohamed, Ghazal Mohammed, Khalifeh Hadil Abu, Khalil Ashraf, Shalaby Ahmed, Dwyer Amy C, Bakr Ashraf M, Keynton Robert, El-Baz Ayman
BioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, USA.
Faculty of Engineering, Abu Dhabi University, Abu Dhabi, UAE.
Proc Int Conf Image Proc. 2020 Oct;2020:355-359. doi: 10.1109/ICIP40778.2020.9190818. Epub 2020 Sep 30.
Recently, studies for non-invasive renal transplant evaluation have been explored to control allograft rejection. In this paper, a computer-aided diagnostic system has been developed to accommodate with an early-stage renal transplant status assessment, called RT-CAD. Our model of this system integrated multiple sources for a more accurate diagnosis: two image-based sources and two clinical-based sources. The image-based sources included apparent diffusion coefficients (ADCs) and the amount of deoxygenated hemoglobin (R2*). More specifically, these ADCs were extracted from 47 diffusion weighted magnetic resonance imaging (DW-MRI) scans at 11 different -values (b0, b50, b100, …, b1000 s/mm), while the R2* values were extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (2ms, 7ms, 12ms, 17ms, and 22ms). The clinical sources included serum creatinine (SCr) and creatinine clearance (CrCl). First, the kidney was segmented through the RT-CAD system using a geometric deformable model called a level-set method. Second, both ADCs and R2* were estimated for common patients (N = 30) and then were integrated with the corresponding SCr and CrCl. Last, these integrated biomarkers were considered the discriminatory features to be used as trainers and testers for future deep learning-based classifiers such as stacked auto-encoders (SAEs). We used a k-fold cross-validation criteria to evaluate the RT-CAD system diagnostic performance, which achieved the following scores: 93.3%, 90.0%, and 95.0% in terms of accuracy, sensitivity, and specificity in differentiating between acute renal rejection (AR) and non-rejection (NR). The reliability and completeness of the RT-CAD system was further accepted by the area under the curve score of 0.92. The conclusions ensured that the presented RT-CAD system has a high reliability to diagnose the status of the renal transplant in a non-invasive way.
最近，人们对用于无创肾移植评估的研究进行了探索，以控制同种异体移植排斥反应。在本文中，开发了一种计算机辅助诊断系统，用于早期肾移植状态评估，称为RT-CAD。该系统的模型集成了多个来源以进行更准确的诊断：两个基于图像的来源和两个基于临床的来源。基于图像的来源包括表观扩散系数（ADC）和脱氧血红蛋白量（R2*）。更具体地说，这些ADC是从47次不同b值（b0、b50、b100、…、b1000 s/mm）的扩散加权磁共振成像（DW-MRI）扫描中提取的，而R2值是从30次不同回波时间（2ms、7ms、12ms、17ms和22ms）的血氧水平依赖性功能磁共振成像（BOLD-MRI）扫描中提取的。临床来源包括血清肌酐（SCr）和肌酐清除率（CrCl）。首先，使用一种称为水平集方法的几何可变形模型通过RT-CAD系统对肾脏进行分割。其次，对普通患者（N = 30）估计ADC和R2，然后将其与相应的SCr和CrCl进行整合。最后，这些整合的生物标志物被视为判别特征，用作未来基于深度学习的分类器（如堆叠自动编码器（SAE））的训练器和测试器。我们使用k折交叉验证标准来评估RT-CAD系统的诊断性能，在区分急性肾排斥（AR）和无排斥（NR）方面，其准确率、灵敏度和特异性分别达到了93.3%、90.0%和95.0%。RT-CAD系统的可靠性和完整性通过曲线下面积得分为0.92进一步得到认可。结论确保了所提出的RT-CAD系统具有以无创方式诊断肾移植状态的高可靠性。
