Beyond Standard Radiotherapy: an AI-Driven Framework for Personalized Radiotherapy with a Four-Step Classification in...

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Radiotherapy (RT) is a primary treatment for head and neck cancer (HNC).
• Personalizing RT has faced challenges, with variable results across treatment stages.

Purpose of the Study:

• To evaluate the potential of AI-driven models for personalized radiotherapy strategies in HNC.
• To assess how AI can bridge existing gaps in personalized RT.

Main Methods:

• Review of radiomics and AI-driven imaging analytics approaches.
• Analysis of AI-driven multimodal and multitemporal imaging models.
• Examination of a proposed four-step AI-driven RT framework.

Main Results:

• Radiomics offers quantitative features for predicting survival, treatment response, and toxicity.
• Radiomics models show high predictive efficacy (C-index/AUC > 0.70).
• AI models can stratify patients and guide risk-adapted radiotherapy.

Conclusions:

• AI-driven radiomics holds significant potential for personalized radiotherapy in HNC.
• AI models can enhance treatment stratification and risk adaptation.
• A standardized AI framework could facilitate future clinical trials and evidence generation.