Deep learning predicts microsatellite instability status in colorectal carcinoma in an ethnically heterogeneous population in South Africa.

Journal: Journal of clinical pathology
Published Date: May 20, 2025

Abstract

BACKGROUND: Deep learning (DL) models are effective pre-screening tools for detecting mismatch repair deficiency (dMMR) in colorectal carcinoma (CRC). These models have been trained and validated on large cohorts from the Northern Hemisphere, without representation of African samples. We sought to determine the performance of a DL model in an ethnically heterogeneous cohort of patients from South Africa.

Authors

  • Alessandro Pietro Aldera
    Division of Anatomical Pathology, University of Cape Town, Observatory, South Africa alessandro.aldera@uct.ac.za.
  • Didem Cifci
    Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany.
  • Gregory Patrick Veldhuizen
    Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
  • Wan-Jung Tsai
    Division of Anatomical Pathology, University of Cape Town, Observatory, South Africa.
  • Komala Pillay
    Division of Anatomical Pathology, University of Cape Town, Observatory, South Africa.
  • Adam Boutall
    Division of General Surgery, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa.
  • Hermann Brenner
    German Cancer Consortium (DKTK), Heidelberg, Germany.
  • Michael Hoffmeister
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Jakob Nikolas Kather
    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
  • Raj Ramesar
    UCT MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences and University of Cape Town, Cape Town, South Africa.

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