SysMIFTA

Background

With over ten thousand people on the waiting list for kidney transplantation, reduction of graft loss is of utmost importance. In the past decades there has been a vast increase of knowledge surrounding the mechanisms involved in acute/active renal allograft rejection, which has brought good therapeutic to the clinic. Unfortunately, the complex cellular interactions resulting in the key components of chronic rejection, interstitial fibrosis and tubular atrophy (IFTA), remain poorly understood. Alternatively activated macrophages play a central role in this complex network of cellular interactions, regulating T-cell behavior in the tissue microenvironment, inducing tissue repair and remodeling effects, and conferring a delicate equilibrium between immunosuppressive beneficial and fibrosis-inducing detrimental effects.

Aim

Key is combining dynamic mathematical models of macrophage-associated immunological and metabolic regulation with advanced biopsy evaluation and in-vitro experiments to dissect processes in the renal interstitium that converge into IFTA.
In our study we combine Deep Learning with multiplex immunohistochemically (mIHC) stained histopathological slides to extract objective information from protocol biopsies with the aim to answer research objectives revolving around kidney allograft rejection.

Media

'Wiskundig wapen tegen afstoting donornier - algoritme kan helpen beschadiging vroeg te herkennen', Mediator (34), ZonMw.

Funding

People

Meyke Hermsen

Meyke Hermsen

PhD Candidate

Computational Pathology Group

Jeroen van der Laak

Jeroen van der Laak

Associate Professor

Computational Pathology Group

Luuk Hilbrands

Luuk Hilbrands

Nephrologist

Nephrology, Radboudumc

Bart Smeets

Bart Smeets

Assistant professor

Pathology, Radboudumc

Friedrich Feuerhake

Friedrich Feuerhake

Pathologist

Pathology, Hannover Medical School

Publications

  • M. Hermsen, T. de Bel, M. van de Warenburg, J. Knuiman, E. Steenbergen, G. Litjens, B. Smeets, L. Hilbrands and J. van der Laak, "Automatic segmentation of histopathological slides from renal allograft biopsies using artificial intelligence", Dutch Federation of Nephrology (NfN) Fall Symposium, 2017. Abstract
  • M. Hermsen, T. de Bel, M. den Boer, E. Steenbergen, J. Kers, S. Florquin, B. Smeets, L. Hilbrands and J. van der Laak, "Glomerular detection, segmentation and counting in PAS-stained histopathological slides using deep learning", Dutch Federation of Nephrology (NfN) Fall Symposium, 2018. Abstract
  • M. Hermsen, T. de Bel, M. den Boer, E. Steenbergen, J. Kers, S. Florquin, J. Roelofs, M. Stegall, M. Alexander, B. Smith, B. Smeets, L. Hilbrands and J. van der Laak, "Deep-learning based histopathologic assessment of kidney tissue", Journal of the American Society of Nephrology, 2019;30(10):1968-1979. Abstract DOI PMID Cited by ~25
  • M. Hermsen, B. Smeets, L. Hilbrands and J. van der Laak, "Artificial intelligence; is there a potential role in nephropathology?", Nephrology Dialysis Transplantation, 2020. Abstract DOI PMID