Uncertainties In Adaptive Radiation Therapy For Patients With Cancers Of The Head And Neck

Date(s) - 04/01/2014
1:00 pm

Jason Pukala, PhD Candidate

Adaptive radiation therapy (ART) is an emerging area of interest within radiation oncology that aims to estimate the doses actually delivered to patients and adapt their treatment plans to mitigate any changes from the planned dose.  Recently introduced technologies, including dose recalculation on repeated volumetric patient imaging and deformable image registration (DIR), are required to perform state-of-the-art ART.  The inherent uncertainties of these new technologies are not well known, however, undermining the confidence in clinical decisions made using these tools.  This work presents a methodology to quantify the uncertainties of ART for the purpose of providing clinicians with a better foundation for making such decisions.

To accomplish this goal, this dissertation is divided into four distinct aims: quantify the dosimetric uncertainty of dose recalculations performed on inter-fraction volumetric patient images, determine the uncertainty introduced by using a plan dose overlay instead of performing dose recalculation, quantify the uncertainty of DIR, and translate the quantified uncertainties into clinically useful tools.  The first aim was satisfied by developing a methodology to quantify the uncertainties of dose recalculation using megavoltage CT that could be extended to other imaging modalities.  For the head-and-neck cancer cases examined, it was found that dose recalculation uncertainties could be maintained within ±2.5% with minimal additional quality assurance effort.  Comparing the plan dose overlay to dose recalculation showed that dose recalculation would be preferred for the most accurate results, but valuable dosimetric trend data could be still be observed if only the dose overlay were available.  A library of ten virtual patient phantoms was developed to quantify the spatial uncertainty of DIR.  The phantoms were derived from images of head-and-neck cancer patients and could be used with any DIR algorithm.  Finally, a method of translating the spatial uncertainty of DIR into dosimetric uncertainty was developed and validated.  Using this method, the dosimetric uncertainties for any head-and-neck patient could be displayed as dose-volume histograms useful for making clinical decisions.  In conclusion, we have developed quality assurance methods for ART that are clinically relevant and report standard metrics that are useful for decision making.