ImaGene

ImaGene integrates radiographic imaging and tumor omics data to identify and validate correlations between imaging-derived phenotypes and genomic markers in tumor research.

Key Features:

• Data Integration: Integrates radiographic imaging datasets with omics data derived from immunohistochemistry and sequencing of biopsy samples to enable joint analysis of imaging features and genetic profiles.
• Correlation Analysis and AI Modeling: Performs statistical correlation analyses between imaging characteristics and genomic markers and constructs artificial intelligence (AI) models to identify and validate genotype–phenotype associations.
• Configurable Parameters: Supports adjustable analysis configuration parameters to tailor statistical and AI modeling settings for specific study designs and datasets.
• Comprehensive Reporting: Generates detailed reports that include model diagnostics and the identified associations for validation and downstream interpretation.
• Reproducible Workflows: Supports reproducible radiogenomic analyses by providing a consistent analytical framework.

Scientific Applications:

• Invasive breast carcinoma (IBC): Demonstrated identification of associations between imaging features and nine genes: WT1, LGI3, SP7, DSG1, ORM1, CLDN10, CST1, SMTNL2, and SLC22A31.
• Head and neck squamous cell carcinoma (HNSCC): Demonstrated identification of associations between imaging features and eight genes: NR0B1, PLA2G2A, MAL, CLDN16, PRDM14, VRTN, LRRN1, and MECOM.

Methodology:

ImaGene accepts tumor omics and imaging datasets as input, performs correlation analyses to identify associations, and constructs artificial intelligence (AI) models to further explore and validate those relationships.