In contrast, functional imaging techniques, such as PET, delve into the bio­chemical and physiological processes occurring within tissues and organs. PET scans, for instance, track the distribution of radiotracers to reveal metabolic activity, offering insights into cellular function that can indicate the presence of disease even before structural changes become apparent. This differentiation between anatomical and functional imaging underscores their complementary roles in medical diagnosis and treatment planning. While anatomical imaging offers a static picture of what is present, functional imaging provides a dynamic view of how the body operates, enabling early detection of diseases based on metabolic changes and aiding in the assessment of treatment efficacy. Together, these imaging modalities furnish a comprehensive understanding of both the form and function of the human body, facilitating precise diagnoses and tailored therapeutic approaches.

In the treatment planning phase, PET data can be invaluable. It aids in delineating tumor boundaries, understanding metabolic hotspots, and designing precise radia­tion beams. Incorporating PET information into daily radiation treatments signifi­cantly enhances the personalization and precision of therapy strategies. By utilizing the detailed metabolic activity and tumor extent data provided by PET scans, clinicians can more accurately define the target areas for radiation, differentiating between cancerous and healthy tissues with greater precision than conventional imaging allows. This detailed insight enables the meticulous adjustment of radiation dose distributions, focusing on eradicating cancer cells while minimizing exposure to surrounding healthy tissues. Furthermore, the concept of ART comes into play, wherein PET imaging is repeatedly used to monitor the tumor’s response to treatment over time. Adjustments to the radiation plan are made dynamically, based on these sequential PET scans, to address changes in tumor size, shape, or metabolic activity. Such an adaptive approach not only aims to improve the accuracy of radiation delivery but also holds the promise of enhancing patient outcomes by reducing treatment-related side effects. Through this integration of PET data, radiation therapy is tailored to the unique characteristics of each patient’s tumor, ensuring a highly individualized treatment process that adapts to the evolving nature of the disease.

AI, combined with PET imaging, can predict patient-specific responses to radiation therapy. These predictive models utilize vast amounts of data to estimate how a tumor might react, allowing for more personalized treatments [24]. It enhances the ability to predict patient-specific responses to radiation therapy by analysing metabolic and physiological data from PET scans. AI algorithms can identify patterns in the data that correlate with treatment outcomes, enabling personalized treatment plans that are more likely to be effective for individual patients. This predictive capability allows for the optimization of radiation doses, minimizing exposure to healthy tissues while targeting tumors more precisely. AI-driven analysis of PET imaging data can lead to better treatment decisions, reduced side effects, and potentially improved survival rates for patients undergoing radiation therapy.