Abstract
This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.
Highlights
Medical imaging modalities have provided an accurate, non-invasive way to detect AR and its underlying cause
Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) uses contrast agents such as gadolinium to evaluate tissue perfusion, which is indicative of renal function[11]
The feasibility of using diffusion-weighted magnetic resonance imaging (DW-MRI) in assessing kidney allograft function has been investigated in several studies[17,18,19,20,21,22,23,24,25,26,27]
Summary
Medical imaging modalities have provided an accurate, non-invasive way to detect AR and its underlying cause. Diffusion weighted (DW) MRI, which is an imaging sequence that does not use contrast agents, has been sucessful in many applications, such as tumor detection and characterization, neuroimaging, and kidney function assessment[16]. The benefit of applying advanced machine learning (ML) techniques was not utilized as a valuable tool for image classification and diagnosis To partially overcome those limitations, recent studies have utilized various ML techniques to improve the diagnostic accuracy of the CAD systems by extracting more learnable features from the underlying data. Yang et al.[34] used CNNs to classify the histological kidney images generated from tissue microarrays that have been obtained using biopsies from tumors and normal cases They achieved a high classification accuracy of 97– 98%. Despite the success of the aforementioned studies[31,33,34,35,36] in building computational models that assess kidney function, they are predicted upon an invasive procedure, i.e. biopsy
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