Evaluating tumor heterogeneity in oncology with genomic-imaging and cloud-based genomic algorithms - Gurulakshmanan, Gurumoorthi; Amarnath, Raveendra N.; Lebaka, Sivaprasad; Reddy, Munnangi Koti; Mohankumar, Nagarajan; Muthumarilakshmi, Surulivelu; Srinivasan, Chelliah (International Journal of Electrical and Computer Engineering (IJECE), 2025)
The goal of this initiative is to rethink how oncology is traditionally practiced by integrating novel approaches to genomic imaging with cloud-based genomic algorithms. The research intends to give a thorough knowledge of cancer biology by focusing on the decoding of tumor heterogeneity as its primary objective. It is possible to get a more nuanced understanding of the intricacy of tumors via the integration of high-resolution imaging tools and sophisticated genetic analysis. It is a pioneering use of cloud computing, which enables the quick analysis of large genomic information. The major goal is to decipher the complex genetic variants that are present inside tumors in order to direct the creation of individualized treatment strategies. This discovery marks a significant step forward, since it successfully bridges the gap between genetics and imaging. Diagnostic accuracy and treatment effectiveness have both been improved. This innovative technique permits real-time analysis, which in turn enables treatment tactics to be adjusted in a timely manner. It makes a significant contribution to the continuous development of oncological research as well as its translation into better clinical outcomes for cancer patients.