A Feasibility Study of Dynamic Adaptive Radiotherapy for Non-Small Cell Lung Cancer

The final state of the tumor at the end of a radiotherapy course is dependent on the doses given in each fraction during the treatment course. This study investigates the feasibility of a novel method, Dynamic Adaptive Radiotherapy (DART) that adapts treatment plans using a dynamic programming technique to consider the expected changes of the tumor in the optimization process. DART allows for non-stationary dose distributions as well as non-stationary fractional doses as needed to achieve a series of optimal plans that are conformal to the tumor over time. We applied this method to the treatment of non-small cell lung cancer and quantified potential dosimetric advantages over conventional IMRT. Sixteen phantom cases with various sizes and locations of tumors and organs-at-risk (OAR) were generated. Each case was planned with DART and conventional IMRT prescribing 60Gy/30fx. The observations of the change in the tumor volume over a treatment course were simulated using two-level cell population model. Monte Carlo simulations of the treatment course for each case were run to account for uncertainty in the tumor response. The same OAR dose constraints were applied for both methods. The frequency of replanning was varied between 1, 2, 5(weekly), and 29(daily). The average tumor maximum, minimum, mean dose, and D95 resulting from DART were 124.0-125.2%, 102.1-114.7%, 113.7-123.4%, and 102.0-115.9% of those from conventional IMRT. The ranges represent the spread in doses as a function of the frequency of replanning. The maximum dose for the cord and esophagus, mean dose for the heart and lungs, and D05 for the unspecified tissues resulting from the plans using DART were 84-102.4%, 99.8-106.9%, 66.9-85.6%, 58.2-78.8%, and 85.2-94.0% of those from conventional IMRT respectively. Significant increases in the tumor dose and reductions in the OAR dose, particularly for parallel OAR with mean or dose-volume constraints, could be achieved using DART