Predicting Clinical Endpoints and Visual Changes with Quality-Weighted Tissue-based Renal Histological Features

medRxiv

Abstract

Abstract Two common obstacles limiting the performance of data-driven algorithms in digital histopathology classification tasks are the lack of expert annotations and the narrow diversity of datasets. Multi-instance learning (MIL) can be used to address the former challenge for the analysis of whole slide images (WSI) but performance is often inferior to full supervision. We show that the inclusion of weak annotations can significantly enhance the effectiveness of MIL while keeping the approach scalable. An analysis framework was developed to process periodic acid-Schiff (PAS) and Sirius Red (SR) slides of renal biopsies. The workflow segments tissues into coarse tissue classes. Handcrafted and deep features were extracted from these tissues and combined using a soft attention model to predict several slide-level labels: delayed graft function (DGF), acute tubular injury (ATI), and Remuzzi grade components. A tissue segmentation quality metric was also developed to reduce the adverse impact of poorly segmented instances. The soft attention model was trained using 5-fold cross-validation on a mixed dataset and tested on the QUOD dataset containing n=373 PAS and n=195 SR biopsies. The average ROC-AUC over different prediction tasks was found to be 0.598±0.011, significantly higher than using only ResNet50 (0.545±0.012), only handcrafted features (0.542±0.011), and the baseline (0.532±0.012) of state-of-the-art performance. Weighting tissues by segmentation quality in conjunction with soft attention has led to further improvement ( AUC = 0.618 ± 0.010). Using an intuitive visualisation scheme, we show that our approach may also be used to support clinical decision making as it allows pinpointing individual tissues relevant to the predictions. 2020 MSC 68T07, 92C50

Authors 7

1. Ka Ho Tam corresponding

   University of Oxford

   Affiliation as printed

   Institute of Biomedical Engineering, University of Oxford

   Institute of Biomedicl Engineering, University of Oxford, Oxford, UK

2. Maria Fernanda Soares

   John Radcliffe Hospital · University of Oxford

   Affiliation as printed

   Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital

   Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK

3. Jesper Kers Aachen Department of Pathology, Leiden Transplant Center

   Leiden University Medical Center · Amsterdam University Medical Centers · University of Amsterdam

   Affiliation as printed

   Department of Pathology, Amsterdam UMC, University of Amsterdam

   Department of Pathology, Leiden Transplant Center, Leiden University Medical Center

   Van ‘t Hoff Institute for Molecular Sciences, University of Amsterdam

   Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands

   Department of Pathology, Leien Transplant Center, Leien University Meical Center, Leien, Netherlans

   Van 't Hoff Institut for Molcular Scincs, Univrsity of Amstrdam, Amstrdam, Nthrlands

4. Edward J. Sharples

   University of Oxford

   Affiliation as printed

   Nuffield Department of Surgical Sciences, University of Oxford

   Nuield Department o Surgical Sciences, University o Oxord, Oxord, UK

5. Rutger J. Ploeg

   NHS Blood and Transplant · University of Oxford

   Affiliation as printed

   Nuffield Department of Surgical Sciences, University of Oxford

   Research and Development, NHS Blood and Transplant Filton and Oxford

   Nuield Department o Surgical Sciences, University o Oxord, Oxord, UK

   Research and Development, NHS Blood and Transplant Filton and Oxford, UK

6. Maria E. Kaisar

   NHS Blood and Transplant · University of Oxford

   Affiliation as printed

   Nuffield Department of Surgical Sciences, University of Oxford

   Research and Development, NHS Blood and Transplant Filton and Oxford

   Nuield Department o Surgical Sciences, University o Oxord, Oxord, UK

   Research and Development, NHS Blood and Transplant Filton and Oxford, UK

7. Jens Rittscher

   University of Oxford

   Affiliation as printed

   Institute of Biomedical Engineering, University of Oxford

   Institute of Biomedicl Engineering, University of Oxford, Oxford, UK