AI-based MSI detection in cancer using digital pathology images offers several benefits compared to genomic analysis. Here are some key advantages:
1. Integration with Standard Pathology Workflow:
– AI-based MSI detection using digital pathology seamlessly integrates into the existing pathology workflow. Pathologists routinely examine tissue slides, and AI augments their capabilities by automating and enhancing the analysis of these images. This integration allows for a smoother adoption of AI in clinical practice.
2. Visual Representation of Morphological Features:
– Digital pathology provides a visual representation of morphological features within tissue samples. AI algorithms can analyze these visual patterns associated with MSI, offering an additional layer of information that complements genomic data. This visual representation can aid in understanding the spatial distribution of MSI-related features within the tissue.
3. Preservation of Tissue Morphology:
– Genomic analysis typically involves DNA extraction and sequencing, which may alter or destroy the tissue morphology. Digital pathology allows for the preservation of the original tissue structure, enabling pathologists and AI algorithms to analyze the spatial relationships and architectural features associated with MSI.
4. Cost-Efficiency and Accessibility:
– Digital pathology is often more cost-effective than genomic analysis, which can be expensive and time-consuming. AI-based MSI detection on digital pathology images allows for efficient, high-throughput analysis that can be more accessible in resource-constrained settings.
5. Reduced Turnaround Time:
– Genomic analysis, especially high-throughput sequencing, may require significant time for sample processing and data interpretation. Digital pathology with AI can offer a quicker turnaround time, allowing for more timely clinical decisions and treatment planning.
6. Multimodal Data Fusion:
– Integrating digital pathology with other imaging modalities, such as radiology or medical imaging, allows for multimodal data fusion. Combining information from different sources can provide a more comprehensive understanding of the disease and contribute to a more accurate assessment of MSI.
7. Real-Time Decision Support:
– AI algorithms applied to digital pathology images can provide real-time decision support to pathologists. This immediate feedback can assist pathologists during diagnosis, helping them make more informed decisions and improving the overall efficiency of the diagnostic process.
8. Clinically Relevant Interpretation:
– Digital pathology focuses on clinically relevant tissue morphology, allowing for the identification of subtle histopathological features associated with MSI. This approach complements genomic analysis by providing actionable information that is directly interpretable in a clinical context.
9. Interpretability and Explainability:
– Digital pathology allows for interpretability and explainability of results, as pathologists can visually inspect and validate the AI-generated findings. This transparency is crucial in gaining the trust of healthcare professionals and ensuring the reliability of diagnostic results.
While genomic analysis provides valuable insights into the molecular characteristics of tumors, the integration of AI with digital pathology images offers a practical and complementary approach for MSI detection, with potential benefits in terms of accessibility, cost, and real-time decision support in clinical settings.