Stochastic optimization of intensity modulated radiotherapy to account for uncertainties in patient sensitivity.
The aim of the present work is to better account for the known uncertainties in radiobiological response parameters when optimizing radiation therapy. The radiation sensitivity of a specific patient is usually unknown beyond the expectation value and possibly the standard deviation that may be derived from studies on groups of patients. Instead of trying to find the treatment with the highest possible probability of a desirable outcome for a patient of average sensitivity, it is more desirable to maximize the expectation value of the probability for the desirable outcome over the possible range of variation of the radiation sensitivity of the patient. Such a stochastic optimization will also have to consider the distribution function of the radiation sensitivity and the larger steepness of the response for the individual patient. The results of stochastic optimization are also compared with simpler methods such as using biological response 'margins' to account for the range of sensitivity variation. By using stochastic optimization, the absolute gain will typically be of the order of a few per cent and the relative improvement compared with non-stochastic optimization is generally less than about 10 per cent. The extent of this gain varies with the level of interpatient variability as well as with the difficulty and complexity of the case studied. Although the dose changes are rather small (<5 Gy) there is a strong desire to make treatment plans more robust, and tolerant of the likely range of variation of the radiation sensitivity of each individual patient. When more accurate predictive assays of the radiation sensitivity for each patient become available, the need to consider the range of variations can be reduced considerably.
Kåver
G
G
Department of Medical Radiation Physics, Karolinska Institutet and Stockholm University, Sweden.
Lind
B K
BK
Löf
J
J
Liander
A
A
Brahme
A
A
D004307:Dose-Response Relationship, Radiation; D005260:Female; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D011836:Radiation Tolerance; D011878:Radiotherapy / Q000009:adverse effects / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted; D013269:Stochastic Processes; D002583:Uterine Cervical Neoplasms / Q000532:radiotherapy