Congo Red Staining in Digital Pathology: The Streamlined Pipeline for Amyloid Detection Through Congo Red Fluorescence Digital Analysis

: Renal amyloidosis is a rare condition caused by the progressive accumulation of misfolded proteins within glomeruli, vessels and interstitium, causing functional decline and requiring prompt treatment due to its significant morbidity and mortality. Congo Red (CR) stain on renal biopsy is the gold standard for the diagnosis, but the need for polarized light is limiting the digitization of this nephropathology field. This study explores the feasibility and reliability of CR fluorescence on virtual slide (CRFvs) evaluating the diagnostic accuracy and proposing an automated digital pipeline for its assessment. Whole slide images (WSI) from 154 renal biopsies with CR were scanned through Texas red fluorescence filter (Nanozoomer S60, Hamamatsu) at the digital Nephropathology Center of IRCCS San Gerardo, Monza, Italy, were evaluated double blinded for the detection and quantification through Amyloid Score (AS) and a custom ImageJ pipeline was built to automatically detect amyloid containing regions. Inter-observer agreement for CRFvs was optimal (k=0.90, 95%CI=0.81-0.98), with even better concordance when consensus-based CRFvs evaluation was compared to the standard CR birefringence (BR, k=0.98, 95%CI=0.93-1). Excellent performance was achieved in the assessment of AS overall by CRFvs (Wk=0.70, 95%CI=0.08-1), especially within the interstitium (Wk=0.60, 95%CI=0.35-0.84), overcoming the misinterpretation of interstitial and capsular collagen birefringence. The application of an automated digital pathology pipeline (Streamlined Pipeline for Amyloid detection through congo red fluorescence Digital Analysis, SPADA) further increased the performance of pathologists, leading to a complete concordance with the standard BR. This study represents an initial step in the validation of CRFvs, demonstrating its general reliability in a digital nephropathology center. The computational method used in this study has the potential to facilitate the integration of spatial omics and artificial intelligence tools for the diagnosis of amyloidosis, streamlining its detection process.