Validation of virtual trichrome stains for kidney fibrosis evaluation using dual-mode emission and transmission microscopy
Ju, W.; Border, S.; Afsari, F.; Seth, S.; Rezapourdamanab, S.; Renteria, R.; Ramachandra, S. S.; Gupta, R.; Salem, F.; Farris, A. B.; Levenson, R.; Zee, J.; Sarder, P.; Jen, K.-Y.; Fereidouni, F.
Show abstract
Assessment of interstitial fibrosis is essential in the diagnosis and prognosis of kidney diseases. However, current histologic scoring methods using trichrome-stained slides are limited by inter-observer variability and inconsistent stain reproducibility. To address these challenges, we developed DUET (DUal-mode Emission and Transmission) microscopy, a novel imaging platform that rapidly captures both brightfield and fluorescence images from H&E-stained slides to generate pixel-registered collagen images and virtual trichrome stains. In a cohort of 32 kidney transplant biopsies, four renal pathologists estimated the extent of interstitial fibrosis in real trichrome and DUET-derived virtual trichrome whole slide images, with the latter showing improved inter-pathologist agreement. A deep learning pipeline was trained to segment interstitial kidney regions from DUET-acquired images, enabling semi-automated computational fibrosis quantitation, which demonstrated a positive correlation with pathologists estimates of interstitial fibrosis. These findings highlight DUET as a rapid, cost-effective, and scalable alternative to traditional trichrome staining, offering both visual and computational advantages.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Detection of infiltrating fibroblasts by single-cell transcriptomics in human kidney allografts 93%
- FalseColor-Python: a rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology 92%
- Evidences of histologic Thrombotic Microangiopathy and the impact in renal outcomes of patients with IgA nephropathy 92%
Similar papers in this journal
- CluSA: Clustering-based Spatial Analysis framework through Graph Neural Network for Chronic Kidney Disease Prediction using Histopathology Images 95%
- Multiple instance learning with pathology foundation models effectively predicts kidney disease diagnosis and clinical classification 95%
- Renal tubular function and morphology revealed in kidney without labeling using three-dimensional dynamic optical coherence tomography 95%
Similar papers in this journal
- Deep learning driven quantification of interstitial fibrosis in kidney biopsies 97%
- Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning 91%
- Single cell transcriptomics reveal disrupted kidney filter cell-cell interactions after early and selective podocyte injury 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.