RENAL ALLOGRAFT BIOPSY IMAGE ANALYSIS TOOLBOX FOR CLINICOPATHOLOGIC CORRELATION
AB Farris, GH Smith, LN Stuart, J Kong, G Tumer, T Roberts-Wilson, C Cohen, DJ Brat, JH Saltz, HM Gebel, RA
Bray, AD Kirk, RA Hennigar
Departments of Pathology, Biomedical Informatics, and Surgery, Emory Transplant Center and Emory University,
Atlanta, Georgia, United States
Objectives: Pathologists and clinicians often rely on assessment of inflammatory cell infiltrates and other features in
renal allograft biopsies when diagnosing rejection and semiquantitative grading of the rejection process (e.g., using
Banff criteria). Slight histologic variations can make big differences in the subsequent treatment course for patients.
However, prior studies have shown limited reproducibility in such evaluations. We tested the utility of
immunohistochemistry (IHC) whole slide image (WSI) analysis in assisting with these designations.
Methods: WSIs were obtained in a cohort of 58 renal biopsies containing allograft rejection, a borderline pattern,
polyomavirus nephritis, normal donor tissue, and stable allografts for a variety of stains. IHC was quantitated using a
positive nuclear IHC algorithm tuned to detect CD3+ cells and a Positive Pixel Count algorithm, yielding a % of
parenchyma with HLA-DR positivity. The CD3+ cell and HLA-DR density was compared with (a) pathologist
assessment of % inflammatory cell infiltrate on routine slides, (b) diagnosis, and (c) other parameters such as
creatinine. In a separate set of biopsies, initial C4d scoring (C4d1, 2, or 3) was verified on repeat pathologic review
of the C4d IHC stained slides. These cases were scanned using a WSI scanner and analyzed with a microvessel
density algorithm, which measured 18 vascular parameters per case. HLA class I and II DSAs for each biopsy were
identified and a cumulative, DSA-specific mean fluorescence intensity (MFI) was calculated.
Results: By linear correlation, the computed CD3+ cell density (# of CD3+ cells/mm2) showed a direct correlation
with the pathologist assessment of % inflammatory infiltrate (r = 0.69, p < 0.0001) and with creatinine (r = 0.70, p =
0.0022). Considering cases with acute cellular rejection (ACR), CD3+ cell density increased from borderline (531), to
ACR1A (673), to ACR1B (1,336). Highest HLA-DR positivity was seen in allograft rejection bxs [Table]. Elevated
levels were also seen in PVN, followed by borderline cases. A baseline HLA-DR level was present in normal donor
bxs, and the lowest levels were in stable allografts. Differences were not significant between rejection and
polyomavirus nephritis; however HLA-DR levels in rejection and PVN were significantly higher than stable and normal
donor bxs. Rejection but not PVN was significantly higher than borderline. Linear regression showed a direct
correlation between total and cortical HLA-DR (r = 0.98, p < 0.0001). C4d scoring and WSI analysis were compared
to the cumulative MFI value (range: 2,600–385,000) for each case. The cumulative MFI was found to be moderately
correlated with human C4d scoring (r = 0.557, p = 0.0071). The cumulative MFI only weakly correlated with
automated WSI analysis, with median vessel perimeter being the strongest correlating parameter (r = 0.367,p =
0.0935). In order to achieve a more accurate reflection of the MFI data, the cases were then stratified into 4 ranks:
“noncontributory”, “low”, “intermediate”, and “high.” This ranked MFI was also found to be moderately correlated with
human C4d scoring (r = 0.643, p=0.0011) and showed improved correlation with automated WSI analysis. Amongst
the WSI analysis output parameters, median vessel perimeter and median vascular area were most correlated with
ranked MFI (r = 0.547, p = 0.0084 and r = 0.523, p = 0.0125, respectively). Weaker correlations were seen with
median lumen area and mean vessel wall thickness (r = 0.505, p=0.0165 and r = 0.386, p = 0.0759, respectively).
HLA-DR total parenchymal
Comparison
Type
positivity (%) [Mean ± S.D.]
(T-test)
Rejection (n = 15)
62.2 ± 16.2
***, *, •
Antibody-mediated rejection (n=3)
68.6 ± 9.3
***, *, •
Cellular rejection (n =12)
60.6 ± 17.4
***, *, •
PVN (n = 3)
60.6 ± 8.1
**, ^
Borderline (n = 2)
36.1 ± 10.4
Normal donor (n = 4)
33.6 ± 18.5
Stable allograft (n =3)
15.0 ± 4.4
S.D.: standard deviation, ***: p < 0.0005 vs. stable, **: p = 0.001 vs. stable, *: p < 0.01 vs. normal donor, ^: p = 0.03
vs. normal donor, •: p < 0.05 vs. borderline
Conclusions: The cell counting algorithm showed correlations with the pathologist assessment of inflammatory
infiltrate as well as creatinine, suggesting its promise in assessing renal allograft biopsies, analogous to flow
cytometry on a slide. HLA-DR IHC quantitation could potentially be objectively useful measuring the immunologic
activation of allografts, particularly when combined WSI segmentation algorithms. Human scoring could still be useful
in some regards since human scoring of C4d deposition provided a better correlation with DSA MFI than automated
image analysis. More sophisticated WSI equipment and algorithms may lead to more accurate assessments in the
future. Future studies using additional multiparameter histologic, IHC, quantum dot, and molecular markers may
prove useful.