Advanced Cancer Imaging with Deep Learning: Unifying Radiomics, Genomics, and Clinical Insights for Precision Diagnosis and Prognosis

Abstract

Cancer possesses challenging layers at the cellular and molecular levels. 
Disease tests today do not measure every disease variation so they cannot 
develop individualized treatment plans. Through deep learning doctors 
work better with all cancer patient data which includes medical scan results 
and biological test outcomes combined with healthcare data. Medical 
imaging scans provide important tumor information by letting radiomics 
use measurements that human eyes cannot see. Researchers learn which 
genetic variations trigger the development of cancer by conducting DNA 
research. Both medical treatments doctors do and facts about their patients 
provide meaning to all medical data. This analysis reviews how deep 
learning systems use combined cancer images and radiomics with medical 
facts to produce better disease prediction results. This research guides 
healthcare facilities on how they can combine these methods to empower 
cancer patients to receive better treatment based on their specific needs.