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Corrigendum for: Artificial Intelligence-Based Digital Image Analysis for Assessing Ki67, P53, and PHH3 Expression in Glioblastoma Multiforme.

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Artificial Intelligence-Based Digital Image Analysis for Assessing Ki67, P53, and PHH3 Expression in Glioblastoma

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Artificial intelligence (AI) analysis of Ki67, PHH3, and p53 biomarkers in glioblastoma multiforme (GBM) showed strong consistency and significant correlations, outperforming traditional pathologist evaluations. This highlights AI

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Area of Science:

  • Oncology
  • Pathology
  • Biomedical Engineering

Background:

  • Glioblastoma multiforme (GBM) is an aggressive brain tumor requiring accurate biomarker assessment.
  • Immunohistochemical (IHC) biomarkers like Ki67, PHH3, and p53 are crucial for GBM diagnosis and prognosis.
  • Conventional pathological evaluation of IHC biomarkers can be subjective and vary between observers.

Purpose of the Study:

  • To compare artificial intelligence (AI)-based IHC analysis of Ki67, PHH3, and p53 in GBM with conventional pathologist evaluations.
  • To assess the consistency and statistical significance of AI and pathologist assessments across different tissue sampling areas.
  • To evaluate the potential of AI in improving the objectivity of biomarker assessment in GBM.

Main Methods:

  • A cross-sectional analytical study involving 20 GBM cases.
  • IHC staining for Ki67, PHH3, and p53 on formalin-fixed, paraffin-embedded tissue sections.
  • Digitization of stained slides for evaluation by two expert pathologists and AI-based image analysis software on 1 mm² and 7 mm² areas.
  • Statistical analysis using Cohen's Kappa for agreement and Spearman's correlation for association.

Main Results:

  • Both AI and pathologist assessments demonstrated strong intra-method consistency across different field sizes.
  • AI analysis revealed significant correlations between Ki67 and PHH3, and between PHH3 and p53 (p <0.05).
  • No significant correlations were found between these markers in pathologist assessments (p >0.05).

Conclusions:

  • AI-supported digital pathology offers a potential improvement in the objectivity and individualization of biomarker assessment for aggressive tumors like GBM.
  • AI analysis demonstrated superior ability to identify correlations between key biomarkers compared to traditional methods.
  • The findings suggest AI can enhance diagnostic accuracy and consistency in GBM biomarker evaluation.