A Robust AI-based Software Platform for Effective Integration of Radiomic & Omics Data of Tumor Patients
The potential for radiomics to support oncology decision-making has grown substantially in recent years, as the tumor image scanning techniques such as Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) and Positron Emission Tomography (PET) offer unique information about tumor phenotype and micro environment complementing the information derived from omics data. Radiomic and omics data can be correlated statistically and modelled together using machine learning techniques to yield valuable information regarding associations between them. Radiogenomically-informed biopsies have a potential to improve pathological outcomes and inform optimal treatment strategies for cancer patients. Currently the field of radiogenomics lacks a unified and robust software platform able to effectively integrate radiomic and omics (e.g. genomics, proteomics) data to facilitate robust AI models able to predict individual omics profiles of tumors from their radiological images. Here we report the development of a comprehensive AI-based platform for effective integration of radiomic and omics data of cancer patients that has a potential to be validated and utilized in clinical settings for effective monitoring, diagnosis, and treatment of cancer patients.
*A novel and comprehensive radiogenomic software that combines the power of statistics and AI together to yield informative reports on associations between radiology images and omics data for patient tumors. Histopathology image data on the radar.
*Easy to use software that accommodates a range of users (naïve to expert).
*Welcoming collaborations from researchers and clinicians worldwide to test the software on multiple tumor types. Potential for clinical deployment in near future.
Shrey Sukhadia leads clinical bioinformatics efforts at Phoenix Children’s Hospital, Phoenix. He has previously led oncology bioinformatics efforts at University of Pennsylvania and participated in research activities at University of Maryland and University of Miami. He is currently pursuing a PhD in Bioinformatics at Queensland University of Technology, Brisbane, Australia and has Masters from University of the Sciences, Philadelphia. His expertise includes Genomic data analysis (somatic and germline), Precision Medicine, Statistics, Artificial intelligence, Radiogenomics, Transcriptomics, Copy number alterations and Software Engineering. Through his PhD research in Radiogenomics he has developed a robust AI-based software for effective integration of radiomic and omics data of cancer patients that has a potential to be validated and utilized in clinical settings for effective monitoring, diagnosis, and treatment of cancer patients. The software has been tested through Glioblastoma Multiforme and Non-Small cell Lung cancer datasets and could be made available to test through multiple cancer types. He is inviting collaborations from researchers and clinicians globally.