The role of texture analysis of T1 weighted images in diagnosis of chronic kidney disease

Chronic kidney disease (CKD) is real and growing, life threatening challenge for modern societies. Nowadays, kidney biopsy is the gold standard for the diagnosis of renal, structural changes, even though it is an invasive procedure with some contraindications and complications. Several studies suggest that microstructural changes during development of kidney disease may give detectable differences in magnetic resonance (MRI) signal, however not always accessible for human perception. The aim of our study was to determine the possibility of using deep neural network algorithms for the analysis of T1-weighted MRI images in the assessment of renal structural changes, particularly in estimating the presence of an active and chronic phases of disease process. The study is based on images of MRI examinations consisting only dixon-based T1-weighted sequence of 52 patients who underwent kidney biopsy and 8 healthy volunteers. The volunteers with no history of any kidney disease formed group “1” and the patients were divided into two groups: group “2”—with active phase of CKD and group “3”—with non-active, advanced phase of CKD, basing on histopathological outcome. The acquired, sorted images were than presented to deep neural network algorithms. Balanced accuracy of verified algorithms in differentiating the study groups were as follows: custom – 83.1%, AlexNet − 81.1%, ResNet-50–93.1%, ViT – 73.5%. The presented algorithms give promise to be efficient, fast and reliable diagnostic tool for patients not suited for kidney biopsy procedure. Additionally, they have potential to be independent of the type of MRI scanner.