Radiotherapy modification based on artificial intelligence and radiomics applied to (¹⁸F)-fluorodeoxyglucose positron...

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Radiation therapy techniques are advancing towards personalized treatment to improve tumor control and reduce toxicity.
• Artificial intelligence (AI), machine learning, and deep learning are transforming medical imaging, including nuclear medicine.
• The concept of a

Purpose of the Study:

• To provide an overview of AI and radiomics in (18F)-FDG PET/CT for cancer radiation therapy.
• To highlight the potential of AI-driven predictive models in personalized radiation oncology.

Main Methods:

• Application of AI and radiomics to (18F)-FDG PET/CT imaging.
• Development of machine learning models for risk stratification and treatment guidance.

Main Results:

• AI and radiomics applied to (18F)-FDG PET/CT offer a powerful tool for predictive modeling.
• These models can assist in stratifying patient risk for radiation therapy.

Conclusions:

• AI and radiomics integrated with (18F)-FDG PET/CT are crucial for advancing individualized radiation therapy.
• These technologies support optimized treatment decisions, enhancing patient outcomes and quality of life.