Cutting-edge imaging techniques combined with machine learning models are reshaping cancer diagnostics. Deep learning frameworks like OncoMet aid in predicting esophageal cancer progression by dissecting oncogenic pathways. Advanced radiomic analyses using PET/CT scans enhance prediction of therapeutic responses in Hodgkin Lymphoma. Additionally, novel biomarkers identified through circRNA and protein studies in esophageal and lung adenocarcinomas offer refined prognostic capabilities. These developments bolster precision oncology by integrating computational algorithms with clinical imaging.