Digital Pathology for Better Clinical Practice

Area of Science:

• Digital pathology and computational pathology
• Cancer research and diagnostics
• Immunohistochemistry and biomarker discovery

Background:

• Digital pathology (DP) revolutionizes traditional slide analysis by enabling high-resolution whole-slide imaging (WSI).
• DP enhances quality assurance, standardization, and remote collaboration for improved diagnostic accuracy.
• Artificial intelligence (AI) in pathology automates tasks, improving cancer diagnosis, classification, prognosis, and biomarker discovery.

Purpose of the Study:

• To highlight the transformative potential of digital pathology and AI in clinical practice.
• To introduce AI-driven immune assays, such as Immunoscore (IS), for assessing tumor immune contexture.
• To demonstrate the superiority of digital IS over traditional methods for cancer patient management.

Main Methods:

• Digitization of glass slides to create whole-slide images (WSI).
• Application of AI for automated analysis of tissue specimens, including spatial analysis of the tumor microenvironment (TME).
• Quantitative assessment of digital Immunoscore (IS) on H&E and CD3+/CD8+ stained colon cancer slides.

Main Results:

• AI-driven Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC) assays improve cancer diagnosis, prognosis, and treatment selection.
• Digital IS quantitative assessment showed higher reproducibility, concordance, and reliability compared to expert pathologists.
• IS outperformed traditional staging systems, demonstrating potential to enhance treatment efficiency and cancer patient care.

Conclusions:

• Digital pathology, integrated with AI technologies like IS, has the potential to revolutionize pathology practices.
• Incorporating AI and digital IS into clinical settings is crucial for advancing personalized cancer therapy.
• Addressing challenges in DP implementation is essential for its successful integration into clinical guidelines.