Enhanced Tumor Diagnostics via Cyber-Physical Workflow: Integrating Morphology, Morphometry, and Genomic MultimodalData Analysis and Visualization in Digital Pathology

TL;DR

This paper introduces a new system combining digital imaging and genomic data to improve early cancer detection and personalized treatment.

Contribution

A novel cyber-physical system integrating morphology, morphometry, and genomic data for enhanced tumor diagnostics.

Findings

The system combines high-resolution tissue scanning and genomic analysis for early cancer detection.

Visualization tools like gene filtering and heatmaps provide insights into genomic heterogeneity.

Integration of multimodal data offers a promising approach to precision oncology.

Abstract

The rapid advancement of genomic technologies has significantly transformed biomedical research and clinical applications, particularly in oncology. Identifying patient-specific genetic mutations has become a crucial tool for early cancer detection and personalized treatment strategies. Detecting tumors at the earliest possible stage provides critical insights beyond traditional tissue analysis. This paper presents a novel cyber-physical system that combines high-resolution tissue scanning, laser microdissection, next-generation sequencing, and genomic analysis to offer a comprehensive solution for early cancer detection. We describe the methodologies for scanning tissue samples, image processing of the morphology of single cells, quantifying morphometric parameters, and generating and analyzing real-time genomic metadata. Additionally, the intelligent system integrates data from open-access genomic databases for gene-specific molecular pathways and drug targets. The developed platform also includes powerful visualization tools, such as colon-specific gene filtering and heatmap generation, to provide detailed insights into genomic heterogeneity and tumor foci. The integration and visualization of multimodal single-cell genomic metadata alongside tissue morphology and morphometry offer a promising approach to precision oncology.

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