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 FTIRbased 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.

Banff Digital Pathology Working Group: Going digital in transplant pathology

“…basic (laboratory) research/science, biopsy, classification systems: Banff classification, clinical research/practice, informatics, organ transplantation in general, pathology/histopathology, rejection For purposes of the meeting and this paper, "digital pathology" refers to a broad collection of computerized techniques applied to pathology, particularly anatomic pathology, including whole slide imaging (WSI), algorithms for dedicated morphometric analysis, algorithms employing artificial intelligence (AI)/machine learning, natural language processing, and novel microscopic techniques (eg, multispectral, Fourier transform infrared and other infrared, and second harmonic generation imaging), which typically employ computerized interfaces. [3][4][5][6][7] This definition is in line with a white paper from the Digital Pathology Association, which defined "digital pathology" as "tools and systems to digitize pathology slides and associated meta-data, their storage, review, analysis, and enabling infrastructure." 8 Furthermore, "digital pathology" can be considered a topic in the larger field of "computational pathology."…”

Label-free molecular imaging of the kidney

“…181 From IR color-coded clustered images, a classification model was developed to automatically detect and quantify renal fibrosis and inflammation (Figure 11). Its efficiency relies on the capability of IR spectroscopy to highlight spectroscopic markers that are specific to both constitutive collagen and fibrosis, yielding a better performance and greater clinical relevance than other techniques such as digital image analysis.…”

Infrared spectroscopic imaging: Label-free biochemical analysis of stroma and tissue fibrosis

“…QCL-IR data has demonstrated differences between fibrotic spectra in liver samples from diabetic and nondiabetic patients spanning the range of hepatic pathology from cirrhosis to hepatocellular carcinoma (Sreedhar et al, 2016). In the context of renal pathology, FT-IR has been able to accurately detect and classify fibrosis, which is a hallmark of kidney damage (Vuiblet et al, 2015). …”

“…It has been shown that IR imaging can rapidly map areas of fibrosis (Cheheltani et al, 2012; Sreedhar et al, 2016; Vuiblet et al, 2015) but new studies demonstrate that additional information also resides within these regions (Kwak et al, 2015). Stromal regions may also permit detection of changes distal to the epithelium (Kumar et al, 2013; Pilling et al, 2017) that remain unaffected by the heterogeneity in the diseased cells; this is useful as that heterogeneity can often interfere with precise diagnosis (Sreedhar et al, 2016).…”