WHAT IS THE MINIMUM NUMBER OF GEOMETRY SAMPLES FOR A STATISTICAL PATIENT MODEL FOR ROBUST TREATMENT PLANNING?

Abstract

Purpose: Organ movement is still the biggest challenge in prostate treatment despite advances in online imaging. Special robust optimization techniques produce organ doses that are insensitive against organ movement. Robust optimization requires a statistical patient model. Based on a finite number of CTs, the movement of the organs is estimated quantitatively. We investigate the interplay of patient model and robust optimization technique. In particular, the minimum number of images necessary to obtain a dependable robust treatment plan is determined. Materials: Starting from N CT images, a statistical shape model of the patient is created by Principle Component Analysis. This statistical information is incorporated into the robust treatment plan optimization. Organ motion gives rise to uncertainty in the treatment outcome parameters (i.e. EUD, etc.). By propagating the organ geometry uncertainty all the way into the treatment outcome parameters, we are able to predict and shape the outcome distributions. This is in contrast to the conventional optimization of a nominal value. Note that the uncertainty in the dose distribution does not necessarily correlate with the uncertainty in treatment outcomes.The basis for our analysis were the CT datasets of four prostate cancer patients, consisting of ~15 images each. For each patient, N CTs were drawn at random from the pool. The patient model based on these CTs was used for optimization. The procedure was rerun 20 times. The resulting dose distributions, along with their respective treatment outcome distributions were analyzed for their similarity.Additionally, it was investigated whether the treatment outcome distributions as predicted by the optimizer coincide with the outcome distributions that are obtained if the model of motion is built using all available imagery, which serves as a gold standard.Both procedures were repeated for varying N. Results: Our analysis indicates that ~5 images suffice to generate a patient model that is able to capture all significant aspects of the patient’s movement. The exact number of required CTs varies from patient to patient, depending on the degree of movement. This suggests an adaptive radiotherapy scheme: a conventional treatment is launched and when enough CBCT images become available, the treatment outcome distributions are calculated. Based on these distributions the plan in place can either be verified or substituted with our robust approach. Conclusions: Our study shows that fully featured statistical optimization is possible under clinical conditions. PCA in conjunction with our optimization technique produces robust treatment plans based on a realistic amount of CTs. Given the small number of required CTs, the outcome distributions can be computed at an early stage of treatment. This allows for the verification as well as correction of existing plans based on probabilistic information.