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Abstract: PO2227

Deep Learning Identifies Pathological Abnormalities Predictive of Graft Loss in Kidney Transplant Biopsies

Session Information

Category: Transplantation

  • 1901 Transplantation: Basic

Authors

  • Yi, Zhengzi, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Salem, Fadi E., Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Menon, Madhav C., Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Xi, Caixia, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Hultin, Sebastian Olof, University of Sydney, Sydney, New South Wales, Australia
  • Haroon Al Rasheed, Mohamed Rizwan, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Li, Li, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Sun, Zeguo, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Wei, Chengguo, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Huang, Weiqing, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Fredericks, Samuel, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Banu, Khadija, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Wong, Germaine, University of Sydney, Sydney, New South Wales, Australia
  • Farouk, Samira S., Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Cravedi, Paolo, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Shingde, Meena, University of Sydney, Sydney, New South Wales, Australia
  • O'Connell, Philip John, University of Sydney, Sydney, New South Wales, Australia
  • Colvin, Robert B., Massachusetts General Hospital, Boston, Massachusetts, United States
  • Murphy, Barbara T., Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Zhang, Weijia, Icahn School of Medicine at Mount Sinai, New York, New York, United States
Background

Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to renal allograft failure. Here we seek an objective, quantitative pathological assessment of these lesions to improve predictive utility.

Methods

We constructed a deep-learning-based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte (MNL) infiltrates from Periodic acid-Schiff (PAS) stained slides of transplant biopsies (training: n=60, testing: n=33) that quantified pathological lesions specific for interstitium, tubules and MNL infiltration. The pipeline was applied to 789 whole slide images (WSI) from baseline (n=478, pre-implantation) and 12-month post-transplant (n=311) protocol biopsies in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss.

Results

Our model accurately recognized kidney tissue compartments and MNLs. The digital features significantly correlated with Banff scores, but were more sensitive to subtle pathological changes below the thresholds in Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of 1-year graft loss (p=2.8e-05), while a Composite Damage Score (CDS) in 12-month post-transplant protocol biopsies predicted later graft loss (p=7.3e-05). ITAS and CDS outperformed Banff score or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITAS or CDS also demonstrated significantly higher incidence of eGFR decline and subsequent graft damage.

Conclusion

This 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.

Funding

  • Other NIH Support