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QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a

Ross J Porter1,2, Shahida Din2, Peter Bankhead1,3

  • 1Edinburgh Pathology, CRUK Scotland Centre, Institute of Genetics and Cancer (IGC), University of Edinburgh, Scotland EH4 2XU, UK.

Diagnostics (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

Automated image analysis using QuPath accurately identifies MLH1-deficient colorectal cancers in inflammatory bowel disease (IBD-CRC). This method improves efficiency and reduces variability in immunohistochemistry analysis for IBD-CRC diagnosis.

Keywords:
MLH1QuPathbiomarkercolorectal cancerhistologyimmunohistochemistryinflammatory bowel diseasemachine learningmismatch repair

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Area of Science:

  • Pathology
  • Computational Pathology
  • Oncology

Background:

  • Immunohistochemistry analysis for MLH1 deficiency in colorectal cancer is labor-intensive and prone to inter-observer variability.
  • Accurate identification of MLH1-deficient inflammatory bowel disease-associated colorectal cancers (IBD-CRC) is crucial for patient stratification and treatment decisions.

Purpose of the Study:

  • To train and validate QuPath, an open-source image analysis software, for automated identification of MLH1-deficient IBD-CRC.
  • To assess the accuracy, sensitivity, and specificity of QuPath in classifying MLH1 expression and tissue histology.

Main Methods:

  • A tissue microarray (n=162) with normal colon and IBD-CRC tissues was immunostained for MLH1.
  • QuPath was trained on a subset (n=14) to differentiate MLH1 expression and tissue types (normal epithelium, tumor, immune infiltrates, stroma).
  • The trained algorithm was applied to the entire tissue microarray, with results compared to manual review.

Main Results:

  • QuPath correctly identified tissue histology and MLH1 expression in 73.74% of valid cases (73/99).
  • The algorithm achieved 100% sensitivity and 98.25% specificity for identifying MLH1-deficient IBD-CRC in classified cores (n=74).
  • High agreement (κ = 0.963) was observed between QuPath classification and manual review, indicating robust performance.

Conclusions:

  • Automated image analysis with QuPath offers an efficient and accurate method for MLH1 expression analysis in IBD-CRC.
  • This approach has the potential to streamline diagnostic workflows in pathology laboratories.
  • QuPath-based analysis can reduce inter-observer variability and improve the consistency of IBD-CRC classification.