Deep learning driven quantification of interstitial fibrosis in kidney biopsies

Abstract

AbstractInterstitial 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.Translational statementPathologists rely on interstitial fibrosis and tubular atrophy (IFTA) to indicate chronicity in kidney biopsies and provide a prognostic indicator of renal survival. Although guidelines for evaluation of IFTA exist, there is variability in IFTA estimation among pathologists. In this work, digitized kidney biopsies were independently reviewed by five nephropathologists and majority voting on their ratings was used to determine the IFTA grade. Using this information, a deep learning model was developed that captured microscopic and holistic features on the digitized biopsies and accurately predicted the IFTA grade. The study illustrates that deep learning can be utilized effectively to perform IFTA grading, thus enhancing conventional clinicopathologic diagnosis.