Precision Strategies for Normal Tissue Radiotoxicity: Biomarkers, AI, and Nanomedicine in Clinical Practice

Area of Science:

• Oncology
• Radiotherapy
• Biotechnology

Background:

• Radiation therapy is a primary treatment for head and neck cancer (HNC), but radiation toxicity significantly impacts patient outcomes.
• Identifying patients at risk for toxicity is crucial for personalized treatment strategies.
• Genetic factors influence individual responses to radiation therapy.

Purpose of the Study:

• To review the role of genetic biomarkers, nanotechnology, and artificial intelligence (AI) in managing radiation-induced normal tissue toxicity in HNC.
• To explore how these technologies can enhance precision and minimize side effects in HNC radiotherapy.
• To assess the potential of integrating these advancements for improved HNC patient care.

Main Methods:

• Comprehensive literature search of PubMed, Scopus, Web of Science, and Google Scholar.
• Narrative review of studies focusing on genetic biomarkers, nanotechnology, and AI in HNC radiation toxicity.
• Analysis of current evidence on predictive and therapeutic applications.

Main Results:

• Genetic biomarkers can predict normal tissue side effects of radiation therapy in HNC patients.
• Nanoparticles offer enhanced tumor targeting and drug delivery, potentially reducing off-target toxicity.
• AI algorithms can personalize radiation therapy plans, improving precision and minimizing damage to healthy tissues.

Conclusions:

• Integrating genetic biomarkers, nanotechnology, and AI holds significant potential to reduce normal tissue toxicity in HNC radiotherapy.
• These innovations can lead to more precise, personalized treatment strategies, improving patient quality of life.
• Addressing current challenges is vital for the successful clinical implementation of these advanced approaches.