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. 2022 Nov 10;2(1):vbac079.
doi: 10.1093/bioadv/vbac079. eCollection 2022.

ImaGene: a web-based software platform for tumor radiogenomic evaluation and reporting

Shrey S Sukhadia  1   2 , Aayush Tyagi  3 , Vivek Venkataraman  1   2 , Pritam Mukherjee  4 , Pratosh Prasad  5 , Olivier Gevaert  4 , Shivashankar H Nagaraj  1   2
Affiliations

ImaGene: a web-based software platform for tumor radiogenomic evaluation and reporting

Shrey S Sukhadia et al. Bioinform Adv. .

Abstract

Summary: Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest, while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Relationships between tumor genotype and phenotype can be identified from these data through traditional correlation analyses and artificial intelligence (AI) models. However, the radiogenomics community lacks a unified software platform with which to conduct such analyses in a reproducible manner. To address this gap, we developed ImaGene, a web-based platform that takes tumor omics and imaging datasets as inputs, performs correlation analysis between them, and constructs AI models. ImaGene has several modifiable configuration parameters and produces a report displaying model diagnostics. To demonstrate the utility of ImaGene, we utilized data for invasive breast carcinoma (IBC) and head and neck squamous cell carcinoma (HNSCC) and identified potential associations between imaging features and nine genes (WT1, LGI3, SP7, DSG1, ORM1, CLDN10, CST1, SMTNL2, and SLC22A31) for IBC and eight genes (NR0B1, PLA2G2A, MAL, CLDN16, PRDM14, VRTN, LRRN1, and MECOM) for HNSCC. ImaGene has the potential to become a standard platform for radiogenomic tumor analyses due to its ease of use, flexibility, and reproducibility, playing a central role in the establishment of an emerging radiogenomic knowledge base.

Availability and implementation: www.ImaGene.pgxguide.org, https://github.com/skr1/Imagene.git.

Supplementary information: Supplementary data are available at https://github.com/skr1/Imagene.git.

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Figures

Fig. 1.
Fig. 1.
ImaGene’s Schema depicting its components
Fig. 2.
Fig. 2.
ImaGene Workflow: calculating correlations, conducting feature selections and training and testing of radiogenomic machine learning models
Fig. 3.
Fig. 3.
(a) Welcome page for ImaGene, (b and c) parameter selection for ImaGene, (d) selectingradiomic and genomic files, and model file depending on the mode of operation, and (e) job tracking and results download page
See this image and copyright information in PMC

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