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 by capturing the pathologic features. Using trichrome-stained whole slide images (WSIs) processed from human renal biopsies, we developed a deep learning (DL) framework that captured finer pathological 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 (OSUWMC).Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: <=10% (None or minimal), 11-25% (Mild), 26-50% (Moderate), and >50% (Severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n=28) obtained from the Kidney Precision Medicine Project (KPMP). There was good agreement on the IFTA grading between the pathologists and the reference estimate (Kappa=0.622±0.071). The accuracy of the DL model was 71.8±5.3% on OSUWMC and 65.0±4.2% on KPMP datasets, respectively. Identification of salient image regions by combining microscopic and WSI-level pathological features yielded visual representations that were consistent with the pathologist-based IFTA grading. Our approach to analyzing microscopic-and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.

Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies

“…The quantification of fibrosis has been the subject of several studies [39][40][41][42]. Artificial neural networks have been developed for the assessment of fibrosis in trichrome-stained kidney slides [43], [44] and recently the first neural network for sclerotic glomeruli and IFTA segmentation in PAS-stained slides was presented,…”

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

“…Another ML pipeline was developed for glomerular localization in whole kidney sections for automated assessment of glomerular injury [ 64 ]. Average precision for glomerular localization was reported as 96.94%, with an average recall of 96.79%.…”

Automatische Bildanalyse und künstliche Intelligenz in der Nephropathologie

“…Der Vorteil solcher Systeme ist, dass direkt eine bestimmte, diagnostisch oder klinisch relevante Information generiert wird. Klassifikationsanwendungen in der Nephropathologie umfassen bislang beispielsweise die Einordnung glomerulärer Läsionen [ 24 , 28 ], die Zuweisung einer diagnostischen Klasse in Nierentransplantatbiopsien [ 19 ] oder die Graduierung der Fibrose in Nierenbiopsien [ 29 ]. Die Nephropathologie profitiert hierbei von anerkannten Definitionen, die dabei helfen, die Trainingsdaten konsistent zu annotieren, beispielsweise für glomeruläre Läsionen [ 11 ] oder für diagnostische Kategorien der Nierentransplantatabstoßung [ 20 ].…”