AI-Driven Quantitative Imaging in Radiotherapy: A New Era of Personalized Dosimetry - Jabouri, Aya Maher Ibrahim; Kamel Mahdi, Athraa Hayder; Al-Zahra Mohammed, Asraa Mohammed Abd; Mohammed, Maryam Abbas Hassan (American Journal of Botany and Bioengineering, 2025)
The evolution of artificial intelligence (AI) has had a significant impact on data acquisition, image reconstruction, and analysis across various fields, including radiotherapy. Concurrently, quantitative imaging has emerged as a formal discipline focused on extracting reliable physical measurements from medical images, thereby enabling a more objective assessment of disease extent. As a result, the synergistic integration of AI and quantitative imaging presents novel opportunities for personalized dosimetry in radiotherapy, potentially leading to improved patient outcomes. Radiotherapy employs ionizing radiation to manage cancers and other benign conditions, with patient-specific dosimetry rules—emanating from clinical trials or consensus guidelines—deciding treatment strategies. Traditional dosimetry approaches, however, lack the capacity to accurately represent the nuanced variability inherent among individual patients. Consequently, AI-driven quantitative imaging offers the potential to quantify personalized signatures of radiation sensitivity and toxicity risk, paving the way for individualized treatment dose adjustments. Drawing on extensive literature and future developments under consideration, this review elucidates the transformative influence of AI in quantitative imaging and its relevance for personalized dosimetry. By facilitating more precise and tailored treatment plans, AI-driven radiographic quantification may enable significant advancements in radiotherapy by enhancing accuracy and reducing adverse side effects.