Future Practices of Breast Pathology Using Digital and Computational Pathology

Area of Science:

• Pathology
• Digital Pathology
• Computational Pathology
• Breast Pathology

Background:

• Breast pathology diagnoses significantly impact patient prognostication and treatment.
• Technological advancements like electron microscopy and genomic sequencing have previously reshaped pathology practice.
• Digital and computational pathology represent the latest wave of innovation.

Purpose of the Study:

• To review the current applications of digital and computational pathology in breast pathology.
• To explore potential future advancements in this field.
• To highlight how these technologies enhance diagnostic accuracy and patient care.

Main Methods:

• Digital slide scanning converts glass slides to high-fidelity digital images.
• Image analysis and machine learning systems are integrated with digital images.
• Review of current literature and emerging trends in digital and computational breast pathology.

Main Results:

• Digital workflows enable specimen diagnosis, archival, collaboration, and telepathology.
• Image analysis and machine learning tools assist in lesion detection, classification, and immunohistochemical quantification.
• Computational biomarkers show potential for improving patient management and outcomes.

Conclusions:

• Digital and computational pathology offer significant advancements for breast pathology.
• These technologies can improve diagnostic accuracy, standardization, and quality assurance.
• Future applications promise enhanced clinical, educational, and research capabilities.