Artificial Intelligence–Based Optimization of Treatment Planning in Intensity‑Modulated Radiation Therapy for Cancer Patients

Intensity‑Modulated Radiation Therapy (IMRT) is a widely used advanced radiotherapy technique that enables highly conformal dose delivery to tumor tissues while minimizing radiation exposure to surrounding healthy organs. However, the treatment planning process for IMRT is complex, time‑consuming, and highly dependent on the expertise of planners. This study aims to investigate the role of artificial intelligence (AI) in optimizing IMRT treatment planning for cancer patients. AI‑based methods, including machine learning and deep learning algorithms, can analyze large datasets of prior treatment plans to predict optimal dose distributions and automate key planning steps such as target delineation, organ‑at‑risk segmentation, and dose optimization. The results of recent research indicate that AI‑assisted planning can significantly reduce planning time while maintaining or improving dosimetric quality compared with conventional manual planning approaches. Additionally, AI models can enhance plan consistency and reduce variability between planners, leading to more standardized treatment outcomes. The integration of AI into IMRT planning workflows also supports the development of personalized radiotherapy strategies tailored to patient‑specific anatomical characteristics. In conclusion, the application of artificial intelligence in IMRT treatment planning has the potential to improve efficiency, accuracy, and clinical outcomes in modern radiation oncology.

کلیدواژه ها:

Artificial Intelligence ، IMRT ، Radiation Therapy Planning ، Machine Learning ، Deep Learning ، Cancer Treatment ، Dose Optimization ، Radiotherapy Planning