Digital image analysis in pathology: benefits and obligation

Area of Science:

• Digital Pathology
• Computational Pathology
• Personalized Medicine

Background:

• The integration of personalized medicine necessitates higher accuracy and precision in tissue-based diagnostics, particularly for quantifying histologic features and biomarker expression.
• Traditional pathology's 'ground truth' is insufficient for current biomarker research and clinical applications.
• Advancements in virtual microscopy and digital image analysis offer automation and reproducibility but not inherent accuracy.

Purpose of the Study:

• To address the need for accurate validation of digital image analysis algorithms in pathology.
• To propose stereology methods as a superior approach for establishing ground truth in digital pathology.
• To advocate for a quantitative mindset shift among pathologists for effective adoption of new technologies.

Main Methods:

• Discussion of the limitations of traditional validation methods, such as pathologists' consensus.
• Argument for the use of stereology methods to validate digital image analysis algorithms.
• Emphasis on the requirement for pathologists to validate and ensure the quality of image analysis algorithms.

Main Results:

• Computerized vision in digital pathology enhances capacity and reproducibility but requires rigorous validation for accuracy.
• Stereology methods provide a reliable means to establish ground truth by estimating the same variables as algorithms.
• A new quantitative approach is essential for pathologists to effectively utilize both traditional and digital microscopy.

Conclusions:

• Accurate validation of digital pathology tools is crucial for reliable tissue-based diagnosis in personalized medicine.
• Stereology offers a robust method for achieving ground truth in digital image analysis.
• Pathologists must adopt a quantitative mentality and master digital tools alongside traditional methods for comprehensive disease assessment.