Home // International Journal On Advances in Life Sciences, volume 17, numbers 1 and 2, 2025 // View article
Authors:
Kellin De Jesus
Leon Dunn
David Thomas
Les Sztandera
Keywords: deep learning; machine learning; quality assurance; volumetric-arc radiation therapy; intensity-modulated radiation therapy
Abstract:
This paper emphasizes emerging strategies in Patient-Specific Quality Assurance (PSQA) for Intensity Modulated Radiotherapy, with particular focus on the use of trajectory log files to enhance computational efficiency and clinical throughput. These log files passively record machine parameters throughout treatment, offering a compelling alternative to conventional phantom-based verification methods, which are resource-intensive and limited in their ability to capture patient-specific variability. Recent advancements have demonstrated the potential of algorithms such as Support Vector Machines, tree-based algorithms, and Artificial Neural Networks to improve the predictive accuracy and robustness of PSQA systems. While current best practices remain essential for ensuring baseline treatment safety, new models should meet additional demands. To maintain high standards of patient care, these models must be explainable, adaptable to evolving clinical workflows, and capable of continuous updates as treatment techniques advance. These attributes are key to enabling clinical integration and establishing a scalable, data-driven framework for personalized, real-time quality assurance in radiation oncology. They are the keystone in turning proof of concept into clinical reality.
Pages: 56 to 66
Copyright: Copyright (c) to authors, 2025. Used with permission.
Publication date: June 30, 2025
Published in: journal
ISSN: 1942-2660