Renal Graft Fibrosis and Inflammation Quantification by an Automated Fourier-Transform Infrared Imaging Technique

Abstract

Renal interstitial fibrosis and interstitial active inflammation are the main histologic features of renal allograft biopsy specimens. Fibrosis is currently assessed by semiquantitative subjective analysis, and color image analysis has been developed to improve the reliability and repeatability of this evaluation. However, these techniques fail to distinguish fibrosis from constitutive collagen or active inflammation. We developed an automatic, reproducible Fourier-transform infrared (FTIR) imaging-based technique for simultaneous quantification of fibrosis and inflammation in renal allograft biopsy specimens. We generated and validated a classification model using 49 renal biopsy specimens and subsequently tested the robustness of this classification algorithm on 166 renal grafts. Finally, we explored the clinical relevance of fibrosis quantification using FTIR imaging by comparing results with renal function at 3 months after transplantation (M3) and the variation of renal function between M3 and M12. We showed excellent robustness for fibrosis and inflammation classification, with >90% of renal biopsy specimens adequately classified by FTIR imaging. Finally, fibrosis quantification by FTIR imaging correlated with renal function at M3, and the variation in fibrosis between M3 and M12 correlated well with the variation in renal function over the same period. This study shows that FTIR-based analysis of renal graft biopsy specimens is a reproducible and reliable label-free technique for quantifying fibrosis and active inflammation. This technique seems to be more relevant than digital image analysis and promising for both research studies and routine clinical practice.

Keywords: acute rejection; chronic allograft nephropathy; histopathology; interstitial fibrosis; renal pathology; renal transplantation.

Figure 1.

Construction of a spectral image by pixel classification into five classes. Each 25×25-μm side pixel has an infrared spectrum that is classified by the LDA model into one of four defined classes (constitutive collagen, fibrosis, glomeruli and tubular sections, and inflammation). The pixels not belonging to any of these four classes are grouped in a fifth class under the heading unclassified.

Figure 2.

Assessment of the robustness of classification by FTIR using the LDA model. (A) Fibrosis and constitutive collagen classes on the FTIR image of each biopsy from the test dataset were blindly compared with Masson trichrome–stained serial sections evaluated by three observers; (B) 93.7% of biopsies were adequately classified, whereas only 6.63% were misclassified.

Figure 3.

Comparison of the IF score and quantification by three techniques. The IF score as estimated by visual inspection according to the Banff classification (IFTA) was compared with the IF score as quantified by (A) DIA or (B) FTIR using the LDA model. Score thresholds were homogenized for all techniques (0<10%; 1=10%–25%; 2=25%–50%; and 3>50%). (C) The IF score by DIA was compared with the IF score as quantified by FTIR, showing poor agreement between these two techniques, whereas (D) IF quantification (percentage) by DIA was correlated with IF quantification by FTIR using the LDA model (P<0.001).

Figure 4.

Assessment of the effect of IF on renal function as quantified by visual rating, DIA, and FTIR quantification. (A) On biopsies performed M3 after transplantation (n=53), the IF score as estimated by visual inspection according to the Banff classification (IFTA) was not associated with the eGFR as estimated by the MDRD (P=0.22). (B) IF quantification by DIA was not correlated with eGFR (P=0.82), whereas (C) the IF quantification by FTIR was significantly correlated with eGFR (P=0.02). (D) The variation in IF quantification between M3 and M12 (Δfibrosis) was compared with the variation in eGFR over the same period (ΔeGFR) for both DIA and FTIR. ΔFibrosis as quantified by DIA was significantly correlated with ΔeGFR (P=0.03). (E) Similarly, Δfibrosis as quantified by FTIR was significantly correlated with ΔeGFR (P<0.001), with a better correlation coefficient.

Figure 5.

Evaluation of the classification robustness of the LDA model for inflammation class and comparison with visual rating. Masson trichrome–stained serial sections of each biopsy from (A) the test set (n=166) were used to blindly compare with (B) the inflammation class on FTIR images; (C) >96% of biopsies were adequately classified. (D) The inflammation score as estimated by visual inspection according to the Banff classification showed poor agreement with the inflammation score as calculated from inflammation quantification by FTIR.