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

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Glioblastoma multiforme (GBM) is an aggressive brain tumor.
• Accurate assessment of proliferation markers like Ki67, P53, and PHH3 is crucial for GBM prognosis.
• Traditional methods for assessing these markers can be time-consuming and subjective.

Purpose of the Study:

• To evaluate the efficacy of AI-based digital image analysis for quantifying Ki67, P53, and PHH3 expression in GBM.
• To explore the potential of AI in improving the accuracy and efficiency of biomarker assessment in neuropathology.

Main Methods:

• The study utilized a dataset of GBM tissue samples.
• AI algorithms were developed and applied to digital whole-slide images for marker quantification.
• Results were compared with conventional assessment methods.

Main Results:

• AI-based analysis demonstrated high concordance with manual scoring for Ki67, P53, and PHH3.
• The AI approach offered a more objective and potentially faster method for biomarker assessment.
• The study identified specific AI-driven features correlating with GBM characteristics.

Conclusions:

• AI-based digital image analysis is a promising tool for assessing proliferation markers in GBM.
• This technology can enhance diagnostic accuracy and prognostic prediction in neuro-oncology.
• Further validation and integration into clinical workflows are warranted.