Background: Contemporary cancer care is increasingly shaped by four simultaneous realities: the growing global cancer burden, the persistent clinical challenge of treatment toxicity, the rising recognition of financial toxicity as a major patient outcome, and the rapid expansion of artificial intelligence for diagnosis, prediction, and decision support. At the same time, host-defense peptides, antimicrobial peptides, and peptide-inspired computational tools are opening new directions for therapeutic innovation. Although these themes are often studied separately, the references provided for this article reveal a strong need for an integrated precision oncology framework.

Objective: This article develops a publication-ready conceptual research study that synthesizes the provided literature into a unified framework connecting cancer epidemiology, patient-reported treatment burden, peptide-based therapeutics, AI-assisted medical imaging, computational prediction systems, and ethical governance in oncology.

Methodology: A structured qualitative integrative review design was used. The analysis was based strictly on the references provided by the author. The literature was grouped into six analytical domains: cancer burden and epidemiology, chemotherapy toxicity and patient burden, financial toxicity, host-defense peptide therapeutics, computational peptide and biomolecular prediction, and AI-based imaging and clinical decision systems. The study then compared these domains to construct a coherent precision oncology model.

Results: The analysis indicates that precision oncology should no longer be understood only as genomic personalization or imaging refinement. Rather, it must be reconceptualized as a multi-layer clinical ecosystem in which diagnosis, therapeutic selection, toxicity prevention, affordability, explainability, and patient-centered outcomes are jointly optimized. AI-based imaging architectures such as U-Net, V-Net, UNet++, Attention U-Net, UNETR, transformer-based systems, and newer long-range dependency models improve lesion localization and segmentation capacity, while computational sequence-based models expand the possibility of identifying peptide-based anti-cancer and anti-inflammatory candidates. However, clinical progress remains incomplete unless these technical gains reduce chemotherapy burden and financial hardship for patients.

Conclusion: The study proposes an integrated AI-enabled precision oncology framework in which diagnostic intelligence, peptide therapeutic innovation, and economic toxicity monitoring are treated as inseparable dimensions of modern cancer care. Future oncology systems must be accurate, biologically grounded, ethically governed, and financially humane.