Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies.

ABSTRACT: Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole-slide images (WSIs) processed from human renal biopsies, we developed a deep-learning framework that captured finer pathologic structures at high resolution and overall context at the WSI level to predict IFTA grade. WSIs (n = 67) were obtained from The Ohio State University Wexner Medical Center. Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grade

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC8453248 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature