BackgroundPathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation.MethodsWe developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally quantify glomerular structure despite its complexity, we simplified it to three components consisting of nuclei, capillary lumina and Bowman spaces; and Periodic Acid-Schiff positive structures. We detected glomerular boundaries and nuclei from whole slide images using convolutional neural networks, and the remaining glomerular structures using an unsupervised technique developed expressly for this purpose. We defined a set of digital features which quantify the structural progression of DN, and a recurrent network architecture which processes these features into a classification.ResultsOur digital classification agreed with a senior pathologist whose classifications were used as ground truth with moderate Cohen’s kappa κ = 0.55 and 95% confidence interval [0.50, 0.60]. Two other renal pathologists agreed with the digital classification with κ1 = 0.68, 95% interval [0.50, 0.86] and κ2 = 0.48, 95% interval [0.32, 0.64]. Our results suggest computational approaches are comparable to human visual classification methods, and can offer improved precision in clinical decision workflows. We detected glomerular boundaries from whole slide images 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.ConclusionsComputationally derived, histologic image features hold significant diagnostic information that may augment clinical diagnostics.

Retroperitoneal fibrosis (RPF) is a rare disease characterised by fibrous tissue proliferation in the retroperitoneum, with encasement of the ureters and large vessels of the abdomen as the most destructive of potentially severe complications. It can either be idiopathic, or secondary to infections, malignancies, or the use of certain drugs. The idiopathic form accounts for approximately 75% of the cases, and is usually responsive to immunosuppressive therapy. In recent years, the emergence of a new clinical entity, IgG4-related disease (IgG4-RD), shed light on many fibro-inflammatory disorders once thought to be separate clinical entities, although frequently associated in the so-called multifocal fibrosclerosis. Among these, together with sclerosing pancreatitis and cholangitis, pseudotumour of the orbit, idiopathic mediastinal fibrosis and other conditions, is idiopathic retroperitoneal fibrosis (IRF). Both IRF and IgG4-RD can be associated with a wide variety of disorders, usually governed by immune-mediated (and particularly auto-immune) mechanisms. In our review, we discuss the clinical and therapeutic challenges IRF presents to the internist, as well as the meaning of its recent inclusion in the IgG4-RD spectrum from a clinical practice standpoint.

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