Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies

Abstract

Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney allograft failure. Here we sought an objective, quantitative pathological assessment of these lesions to improve predictive utility and constructed a deep-learning-based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte infiltrates. Periodic acid- Schiff stained slides of transplant biopsies (60 training and 33 testing) were used to quantify pathological lesions specific for interstitium, tubules and mononuclear leukocyte infiltration. The pipeline was applied to the whole slide images from 789 transplant biopsies (478 baseline [pre-implantation] and 311 post-transplant 12-month protocol biopsies) in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. Our model accurately recognized kidney tissue compartments and mononuclear leukocytes. The digital features significantly correlated with revised Banff 2007 scores but were more sensitive to subtle pathological changes below the thresholds in the Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of one-year graft loss, while a Composite Damage Score in 12-month post-transplant protocol biopsies predicted later graft loss. ITASs and Composite Damage Scores outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITASs or Composite Damage Scores also demonstrated significantly higher incidence of estimated glomerular filtration rate decline and subsequent graft damage. Thus, our deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies and demonstrated superior ability for prediction of post-transplant graft loss with potential application as a prevention, risk stratification or monitoring tool.

Keywords: deep learning; graft survival; kidney transplantation; renal pathology.

Figure 1 |. Study design.

This study consists of 2 major stages. Stage I is tissue compartment recognition. Ninety-three slides that represented the spectrum of histologic lesions were selected from the Genomics of Chronic Allograft Rejection (GoCAR) periodic acid–Schiff (PAS) slides and then randomly divided into the discovery set (n = 60) and the testing set (n = 33). The annotated sections of these slides were used for deep-learning model construction and evaluation. During the training process, we built the models based on 2 types of deep-learning structures for compartment or mononuclear leukocyte (MNL) detection (by mask region-base convolution neural network [MRCNN]) and tissue segmentation (by U-Net). Models were determined through evaluation with 10-fold cross-validation and finally applied to the testing set. Stage II is the whole-slide image (WSI) clinical investigation. Using the established deep-learning model, we processed 789 baseline and 12-month post-transplantation (post-tx) WSIs from 2 independent cohorts (GoCAR and Australian Chronic Allograft Dysfunction [AUSCAD]) and extracted a series of slide-wide digital features capturing the abnormalities in the interstitium and tubules, and MNL infiltration. These features were further examined through association with Banff scores and post-transplantation graft survival. bx, biopsies; FC, fully connected; RPN, region proposal network.

Figure 2 |. Demonstration of slide-wide digital features and correlation with corresponding Banff scores.

(a) Demonstration of slidewide digital features from the whole-slide image (WSI) investigation by an example WSI: (i) original WSI; (ii) whole-slide prediction; (iii) predicted abnormal interstitium or tubules regions of interest (ROIs); (iv) predicted mononuclear leukocytes (MNLs) infiltrated ROIs. Left panel shows zoom-in inspections of 1 particular abnormal region within the yellow box on the WSI. (b) Correlation of digital features with Banff scores. Correlation of abnormal interstitial area percentage and Banff ci score (top), abnormal tubules density and Banff ct score (middle), MNL-enriched area percentage and Banff ti score (bottom) in the Genomics of Chronic Allograft Rejection (GoCAR) 12-month post-transplantation biopsy slides (n = 200). P values were calculated from Spearman’s correlation test. To optimize viewing of this image, please see the online version of this article at www.kidney-international.org.

Figure 3 |. Association of baseline digital features with post-transplant graft outcomes in the Genomics of Chronic Allograft Rejection (GoCAR) cohort.

(a) Heat map of time-dependent area under the curve (AUC) values in predicting death-censored graft loss (DCGL) by Banff scores and digital features at different time intervals in baseline biopsy slides (n = 317). Numbers and yellow-red color range of boxes represent AUC values at given time points. (b) Kaplan-Meier curves of DCGL in high, intermediate, and low risk groups stratified by the Interstitial and Tubular Abnormality Score (ITAS) from baseline biopsies (n = 317). Baseline ITAS groups are defined as high, ITAS > 0.6; intermediate, 0.1 ≤ ITAS ≤ 0.6; and low, ITAS < 0.1. P values are calculated by log-rank test. (c) Average estimated glomerular filtration rate (eGFR) values over time within 12-months post-transplantation per baseline ITAS risk group. Error bars represent ×0.1 SD from mean values. (d) Bar charts demonstrating proportions of delayed graft function (DGF) and no DGF (upper) and 3-month post-transplant Chronic Allograft Damage Index (CADI) >2 or ≤2 (lower) among 3 baseline ITAS risk groups. P values are calculated by Fisher’s exact test.

Figure 4 |. Association of 12-month post-transplant digital features with post-transplant graft outcomes in the Genomics of Chronic Allograft Rejection (GoCAR) cohort.

(a) Heat map of time-dependent area under the curve (AUC) values in predicting death-censored graft loss (DCGL) by Banff scores and digital features at different time intervals in 12-month post-transplant biopsy slides (n = 200). Numbers and yellow-red color range of boxes represent AUC values at given time points. (b) Heat map of time-dependent AUCs in predicting DCGL by the 12-month Composite Damage Score ([CDS], capturing the interstitial and tubular abnormality and mononuclear leukocyte [MNL] infiltration) high or low group and other pathologic or clinical (or both) factors that were obtained prior to or at 12 months. The 12-month CDS groups are defined as high, CDS > 1.5, and low, CDS ≤ 1.5. (c) Kaplan-Meier curves of the DCGL in high and low risk groups stratified by the 12-month CDS. P value is calculated by log-rank test. (d) Bar charts demonstrating proportions of 6-month to 24-month estimated glomerular filtration rate (eGFR) decline ≥30% or <30% (upper) and the 24-month post-transplant Chronic Allograft Damage Index (CADI) >2 or ≤2 (lower) between 12-month CDS risk groups. P values are calculated by Fisher’s exact test.