Towards optimal stopping in radiation therapy. (May 2019)

• Record Type: Journal Article
• Authors: Ajdari, Ali; Niyazi, Maximilian; Nicolay, Nils Henrik; Thieke, Christian; Jeraj, Robert; Bortfeld, Thomas
• Is Part Of: Radiotherapy and oncology. Volume 134(2019)
• Page Start: 96
• Page End: 100
• Publication Date: 2019-05
• Subjects: Optimal stopping -- OSRT -- Predictive biomarker -- Bayesian learning -- Mathematical optimization -- Adaptive radiotherapy; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2019.01.010
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: Mathematical tools can help assess treatment efficacy on the fly. Balance the competition of NTCP vs. TCP dynamically. Stopping may refer to transitioning to a more effective mode of treatment. Optimally stop or transition, not too early, not too late. Abstract: A typical fractionated radiotherapy (RT) course is a long and arduous process, demanding significant financial, physical, and mental commitments from patients. Each additional session of RT significantly increases the physical and psychological burden on patients and leads to higher radiation exposure in organs-at-risk (OAR), while, in some cases, the therapeutic benefits might not be high enough to justify the risks. Today, through technological advancements in molecular biology, imaging, and genetics more information is gathered about individual patient response before, during, and after the treatment. we highlight some of the ways that mathematical tools can help assess treatment efficacy on the fly, adapt the treatment plan based on individual biological response, and optimally stop the treatment, if necessary. We term this "Optimal Stopping in RT (OSRT)", after a similar concept in the fields of dynamic programming and Markov decision processes. In short, OSRT can dynamically determine "whether, when and how" to stop the treatment to improve therapeutic ratios.