Background: Quantitative assessment of renal fibrosis on biopsy remains a time-consuming step and is subject to staining inconsistencies, interobserver variability, and sampling errors, given the heterogeneous nature of renal fibrosis. An objective, rapid, and point-of-care method at multiple locations in the kidney is particularly needed for pre-implantation donor kidney assessment of fibrosis, as the non-use of ∼ 20% of grafts is linked to erroneous fibrosis assessment. We investigated the accuracy of an orthogonal technique—intrarenal Elastic Scattering Spectroscopy (ESS) and compared it against the conventional interstitial fibrosis and tubular atrophy (IFTA) scale. Methods: In a rat model of chronic kidney disease, a customized fine-needle ESS probe was used to acquire spectral data at three predetermined intrarenal depths, followed by standard histopathological scoring. Linear regression was initially used to correlate ESS-derived spectral features with expert-reported IFTA scores. Subsequently, both continuous and categorical random forests (RFs) were implemented to predict fibrosis severity. Results: Rat kidneys showed progressive fibrosis over 4 weeks. RF regression applied to the 300–500 nm spectral range yielded R 2 values of 0.925, 0.917, and 0.838 at depths of 1.5 mm, 3 mm (renal cortex), and 5 mm (corticomedullary junction and medulla) depths, respectively. Averaging across depths increased the R 2 to 0.961. Corresponding categorical RF accuracies were 98.14%, 96.74%, and 93.49%, with an averaged overall accuracy of 98.61% following five-fold cross-validation. Conclusions: Intrarenal ESS provides a robust, depth-resolved, and quantitative estimation of fibrosis that closely parallels histological IFTA scoring. Multi-depth sampling improves predictive accuracy by minimizing local variability. ESS represents an objective, rapid adjunct for point-of-care tissue evaluation at multiple sites within the kidney, without the need for a biopsy or inflicting any complications.
Lotfollahzadeh et al. (Tue,) studied this question.