New approach in lung cancer radiotherapy offers better normal tissue sparing

Medical images are more than pictures. They contain additional quantitative information which can be interrogated, quantified, and utilized. Besides anatomical information computed tomography (CT) imaging data provide electron density information. Radiotherapy use of this density information is limited to its application only in dose calculations. The direct product of dose, density, and volume forms a quantity called integral dose. The integral dose delivered to a volume of interest is the total energy deposited in that volume. Here it is hypothesized that minimization of the integral dose is advantageous in radiotherapy planning. The purpose of this work is to study the incorporation of quantitative imaging information in radiotherapy inverse optimization through total energy minimization (Energy hereafter).

Twenty lung patient plans were studied. For each patient density was quantified on voxel-by-voxel basis through image gray value-to-density conversion curves. Energy-based objective function was used for inverse radiotherapy plan optimization. The obtained plans were evaluated in the light of current standard of care, based on dose-volume (DVH) optimization approach.

The statistical significance analyses of the results indicated that the doses to normal tissue were between 14% and 45% lower, when Energy-based optimization was used instead of DVH-based optimization.

Incorporation of quantitative imaging information, through CT derived density, in the optimization cost function allows reduction of dose to normal tissue for NSCLC cases. Energy-based radiotherapy plans result in lower normal tissue dose and potentially lower complication rates compared to standard of care.