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Background: The vast majority of polyomavirus nephropathy (PVN) is due to BK virus, but rare cases result from JC virus reactivation. To date, only a handful of biopsy-proven JC-PVN cases have been reported. Here, we describe the clinical and pathologic findings in 7 patients with biopsy-proven JC-PVN., Methods: Search of the pathology archives at 2 institutions found 7 cases of JC-PVN. Clinical data were extracted from the electronic medical records, and the biopsies were reviewed., Results: Four cases were diagnosed at 6 y posttransplant or later. The remaining 3 cases presented within approximately 2 y posttransplant, of which 2 showed subclinical JC-PVN on surveillance biopsy. Two early presenting patients were treated for acute rejection just before acquiring JC-PVN. Late presenting patients had higher chronicity, which correlated to worse outcome. All but 1 biopsy showed nonspecific inflammation within areas of interstitial fibrosis without significant inflammation in unscarred cortex. The earliest presenting patient was the exception and showed active inflammation with tubulitis. Viral cytopathic changes were detected in all cases with moderate or high-histologic viral load (pvl), showing preference for the distal tubules and medulla. The 2 cases with low pvl did not demonstrate cytopathic changes but were SV40 positive., Conclusions: JC-PVN can be insidious in presentation, which may cause delayed or missed diagnosis. Unlike BK-PVN, which typically occurs early in the posttransplant period, JC-PVN can occur both early and late following transplant. Overreliance on negative plasma and urine BK viral loads to exclude PVN can be a pitfall., Competing Interests: The authors declare no funding or conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Histologic examination of interstitial fibrosis and tubular atrophy (IFTA) is critical to determine the extent of irreversible kidney injury in renal disease. The current clinical standard involves pathologist's visual assessment of IFTA, which is prone to inter-observer variability. To address this diagnostic variability, we designed two case studies (CSs), including seven pathologists, using HistomicsTK- a distributed system developed by Kitware Inc. (Clifton Park, NY). Twenty-five whole slide images (WSIs) were classified into a training set of 21 and a validation set of four. The training set was composed of seven unique subsets, each provided to an individual pathologist along with four common WSIs from the validation set. In CS 1, all pathologists individually annotated IFTA in their respective slides. These annotations were then used to train a deep learning algorithm to computationally segment IFTA. In CS 2, manual and computational annotations from CS 1 were first reviewed by the annotators to improve concordance of IFTA annotation. Both the manual and computational annotation processes were then repeated as in CS1. The inter-observer concordance in the validation set was measured by Krippendorff's alpha (KA). The KA for the seven pathologists in CS1 was 0.62 with CI [0.57, 0.67], and after reviewing each other's annotations in CS2, 0.66 with CI [0.60, 0.72]. The respective CS1 and CS2 KA were 0.58 with CI [0.52, 0.64] and 0.63 with CI [0.56, 0.69] when including the deep learner as an eighth annotator. These results suggest that our designed annotation framework refines agreement of spatial annotation of IFTA and demonstrates a human-AI approach to significantly improve the development of computational models.