Probabilistic Incorporation of Uncertainties in Radiation therapy
External beam radiotherapy (RT) is one of the leading modalities of treating cancer. Delivering a sufficient radiation dose to the tumor to allow for local disease control, while sparing nearby healthy tissues is the focus of much active research today. The ICRU Reports 50, 62 and 83, which have established precise terminology to describe different areas of tumor presence, define current RT clinical paradigm through the use of GTV/CTV/PTV and expansions of volumes by margins. Although such approach is pragmatic and historically sensible, simply adding margins in such a linear way is a method that limits the implementation of non-uniform dose techniques, such as dose painting by the numbers, where a higher radiation dose is delivered to the more active volume of the tumor.
We propose to create a new workflow of accounting for uncertainties. Rather than using binary tumor segmentations that already introduce a “rounding error” in the first step, we propose to use probabilistic mapping to describe tumor location. In parallel to that, the uncertainty information is maintained throughout the planning process and used in treatment planning itself. Lastly, robust optimization (RO) will be used for the final optimization of radiation dose. RO is an optimization method that optimizes for a “reasonable worst-case scenario”, rather than a nominal case. In this context, we can use the methodology to create numerous scenarios with various realizations of errors and evaluate the scenario performing the worst. This allows for much more realistic variability in error propagation, as well as accounts for natural robustness of certain beams to certain types of errors.
Quantification of intratumor heterogeneity
I am also interested in voxel-to-voxel progression across several timepoints of the same lesion and corresponding changes in CT density and PET values. Tumors are known to be heterogeneous structures and understanding the variability of tissue behavior inside a single lesion could greatly improve clinical outcomes.
Axial pelvic slice with red color showing volumes of increasing SUV and blue showing areas of decreasing SUV