Enhanced Tumor Diagnostics via Cyber-Physical Workflow: Integrating Morphology, Morphometry, and Genomic...

Area of Science:

• Biomedical Engineering
• Genomics
• Oncology

Background:

• Genomic technologies are revolutionizing oncology, enabling personalized treatments.
• Early cancer detection via genetic mutation identification is critical.
• Traditional tissue analysis has limitations in early tumor detection.

Purpose of the Study:

• To present a novel cyber-physical system for comprehensive early cancer detection.
• To integrate high-resolution imaging, genomic sequencing, and data analysis.
• To advance precision oncology through multimodal data integration.

Main Methods:

• Developed a cyber-physical system combining tissue scanning, laser microdissection, and next-generation sequencing.
• Implemented image processing for single-cell morphology and morphometric parameter quantification.
• Integrated open-access genomic databases for pathway and drug target analysis.

Main Results:

• Generated and analyzed real-time genomic metadata from tissue samples.
• Utilized visualization tools (gene filtering, heatmaps) for genomic heterogeneity insights.
• Demonstrated integration of single-cell genomic data with tissue morphology.

Conclusions:

• The developed platform offers a comprehensive solution for early cancer detection.
• Multimodal data integration provides deeper insights into tumor characteristics.
• This approach shows significant promise for advancing precision oncology.