The field of radiomics has undergone several advancements in approaches to uncovering hidden quantitative features from tumor imaging data for use in guiding clinical decision-making for cancer patients. Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest (ROIs), while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. These imaging and omics feature data can then be correlated and modeled using artificial intelligence (AI) techniques to highlight notable associations between tumor genotype and phenotype. Currently, however, the radiogenomics field lacks a unified and robust software platform capable of algorithmically analyzing imaging and omics features using modifiable parameters, detecting significant relationships among these features, and subjecting them to AI-based analysis. To address this gap, we developed ImaGene, a robust AI-based platform that uses omics and imaging features as inputs for different tumor types, performs statistical analyses of the correlations between these data types, and constructs AI models based upon significantly correlated features. It has several modifiable configuration parameters that provide users with complete control over their experiments. For each run, ImaGene produces comprehensive reports that can contribute to the construction of a novel radiogenomic knowledge base, in addition to enabling the deployment and sharing of AI models. To demonstrate the utility of ImaGene, we acquired imaging and omics datasets pertaining to Invasive Breast Cancer (IBC) and Head and Neck Squamous Cell Carcinoma (HNSCC) from public databases and analyzed them with this platform using specific parameters. In both cases, we uncovered significant associations between several imaging features and 11 genes: CRABP1, VRTN, SMTNL2, FABP1, HAND2, HAS1, C4BPA, FAM163A, DSG1, SMTNL2 and KCNJ16 for IBC, and 10 genes: CEACAM6, IGLL1, SERPINA1, NANOG, OCA2, PRLR, ACSM2B, CYP11B1, and VPREB1 for HNSCC. Overall, our software platform is capable of identifying, analyzing, and correlating important features from tumor scans, thereby providing researchers with a reliable and accurate tool for their radiogenomics experiments. We anticipate that ImaGene will become the gold standard for tumor analyses in the field of radiogenomics owing to its ease of use, flexibility, and reproducibility. Citation Format: Shrey S. Sukhadia, Aayush Tyagi, Vivek Venkatraman, Pritam Mukherjee, Prathosh A.P., Mayur Divate, Olivier Gevaert, Shivashankar H. Nagaraj. ImaGene: A robust AI-based software platform for tumor radiogenomic evaluation and reporting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6341.
The field of radiomics has undergone several advancements in approaches to uncovering hidden quantitative features from tumor imaging data for use in guiding clinical decision-making for cancer patients. Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest (ROIs), while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Potential associations between tumor genotype and phenotype can be identified from imaging and omics data via traditional correlation analysis, as well as through artificial intelligence (AI) models. However, at present the radiogenomics community lacks a unified software platform for which to conduct such analyses in a reproducible manner.To address this gap, we propose ImaGene, a web-based platform that takes tumor omics and imaging data sets as input, performs correlation analysis between them, and constructs AI models (optionally using only those features found to exhibit statistically significant correlation with some element of the opposing dataset). ImaGene has several modifiable configuration parameters, providing users complete control over their analysis. For each run, ImaGene produces a comprehensive report displaying a number of intuitive model diagnostics.To demonstrate the utility of ImaGene, exploratory studies surrounding Invasive Breast Carcinoma (IBC) and Head and Neck Squamous Cell Carcinoma (HNSCC) on datasets acquired from public databases are conducted. Potential associations are identified between several imaging features and 6 genes: CRABP1, SMTNL2, FABP1, HAS1, FAM163A and DSG1 for IBC, and 4 genes: CEACAM6, NANOG, ACSM2B, and UPK2 for HNSCC.In summary, the software provides researchers with a transparent tool for which to begin radiogenomic analysis and explore possible further directions in their research. We anticipate that ImaGene will become the standard platform for tumor analyses in the field of radiogenomics due to its ease of use, flexibility, and reproducibility, and that it can serve as an enabling centrepoint for an emerging radiogenomic knowledge base.
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