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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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Abstract

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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Abbreviations

ACR:

Acute cellular rejection

ACR1A:

Acute cellular rejection, type 1A

ACR1B:

Acute cellular rejection, type 1B

ACR2A:

Acute cellular rejection, type 2A

ACR3:

Acute cellular rejection, type 3

PPC:

Positive pixel count

WSI:

Whole slide image

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Acknowledgements

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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Authors and Affiliations

  1. Department of Pathology, Emory University, Atlanta, GA, USA

    Andres Moon, Geoffrey H. Smith, Thomas E. Rogers, Carla L. Ellis & Alton B. “Brad” Farris III

  2. Department of Bioinformatics, Emory University, Atlanta, GA, USA

    Jun Kong

  3. Emory University Hospital, 1364 Clifton Road NE, Room H-188, Atlanta, GA, 30322, USA

    Alton B. “Brad” Farris III

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  1. Andres Moon
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  2. Geoffrey H. Smith
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  3. Jun Kong
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  6. Alton B. “Brad” Farris III
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Correspondence to Alton B. “Brad” Farris III.

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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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