A Renal Revolution

Kidney disease diagnosis is not always easy – renal pathology is an uncommon specialty and reading kidney biopsies can present a challenge. But what if computational pathology could help improve the diagnostic process and the accuracy of the ultimate diagnosis? Two recent studies have taken on the task of building a better mousetrap by creating computational tools to tackle areas of difficulty in kidney disease diagnosis.

The first study, performed by researchers at the University at Buffalo, combined image analysis and machine learning into a digital algorithm to classify renal biopsies from patients with diabetic nephropathy. Although glomerular structure is complex and can be difficult for even human pathologists to fully quantify, the researchers provided a set of simplified components for the algorithm to use in its classification. Ultimately, the digital classifications of biopsies from 54 diabetic nephropathy patients substantially agreed with those of three different human pathologists – and the algorithm was able to detect “glomerular boundaries […] with 0.93±0.04 balanced accuracy, glomerular nuclei with 0.94 sensitivity and 0.93 specificity, and glomerular structural components with 0.95 sensitivity and 0.99 specificity (1).”

The second study, conducted at Radboud University Medical Center, used a convolutional neural network to extend the analysis to multiple tissue classes in kidney transplant biopsies – an area where work is already time-consuming and unreliable (2). The network’s performance varied depending on the tissue type; glomeruli were a particular strength, with the network detecting 92.7 percent of all glomeruli and exhibiting a 10.4 percent false positive rate (3). Although there remains room for improvement, this first convolutional neural network of its type heralds future possibilities for deep learning and neural networks in the day-to-day diagnostic workflow.

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References

1. B Ginley et al., “Computational segmentation and classification of diabetic glomerulosclerosis”, J Am Soc Nephrol, 30, 1953 (2019). PMID: 31488606.
2. S Mohan, “Wasting the Gift of Life?”, The Pathologist (2019). Available at: https://bit.ly/2BbIa6e.
3. M Hermsen et al., “Deep learning-based histopathologic assessment of kidney tissue”, J Am Soc Nephrol, 30, 1968 (2019). PMID: 31488607.

About the Author(s)

Michael Schubert

While obtaining degrees in biology from the University of Alberta and biochemistry from Penn State College of Medicine, I worked as a freelance science and medical writer. I was able to hone my skills in research, presentation and scientific writing by assembling grants and journal articles, speaking at international conferences, and consulting on topics ranging from medical education to comic book science. As much as I’ve enjoyed designing new bacteria and plausible superheroes, though, I’m more pleased than ever to be at Texere, using my writing and editing skills to create great content for a professional audience.

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