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Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software

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Renal allograft rejection diagnosis depends on assessment of parameters such as interstitial inflammation; however, studies have shown interobserver variability regarding interstitial inflammation assessment. Since automated image analysis quantitation can be reproducible, we devised customized analysis methods for CD3+ T-cell staining density as a measure of rejection severity and compared them with established commercial methods along with visual assessment. Renal biopsy CD3 immunohistochemistry slides (n = 45), including renal allografts with various degrees of acute cellular rejection (ACR) were scanned for whole slide images (WSIs). Inflammation was quantitated in the WSIs using pathologist visual assessment, commercial algorithms (Aperio nuclear algorithm for CD3+ cells/mm2 and Aperio positive pixel count algorithm), and customized open source algorithms developed in ImageJ with thresholding/positive pixel counting (custom CD3+%) and identification of pixels fulfilling “maxima” criteria for CD3 expression (custom CD3+ cells/mm2). Based on visual inspections of “markup” images, CD3 quantitation algorithms produced adequate accuracy. Additionally, CD3 quantitation algorithms correlated between each other and also with visual assessment in a statistically significant manner (r = 0.44 to 0.94, p = 0.003 to < 0.0001). Methods for assessing inflammation suggested a progression through the tubulointerstitial ACR grades, with statistically different results in borderline versus other ACR types, in all but the custom methods. Assessment of CD3-stained slides using various open source image analysis algorithms presents salient correlations with established methods of CD3 quantitation. These analysis techniques are promising and highly customizable, providing a form of on-slide “flow cytometry” that can facilitate additional diagnostic accuracy in tissue-based assessments.

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Acute cellular rejection


Acute cellular rejection, type 1A


Acute cellular rejection, type 1B


Acute cellular rejection, type 2A


Acute cellular rejection, type 3


Positive pixel count


Whole slide image


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Special thanks are given to the laboratories of the Emory University Department of Pathology. Thanks also to Dr. Mingqing Song of Emory University and Duke University for help in whole slide scanning.

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Correspondence to Alton B. “Brad” Farris III.

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This study was reviewed and approved by the Emory University Institutional Review Board.

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The authors declare that they have no conflict of interest.

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Moon, A., Smith, G.H., Kong, J. et al. Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software. Virchows Arch 472, 259–269 (2018). https://doi.org/10.1007/s00428-017-2260-6

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  • Renal allograft
  • Image analysis
  • Immunohistochemistry
  • Whole slide image
  • Rejection