Establishing action threshold for change in patient anatomy using EPID gamma analysis and PTV coverage for head and neck radiotherapy treatment.

PURPOSE

To present a new adaptive radiotherapy (ART) method based on relative gamma analysis and patient classification for the identification of anatomical changes that induce a sufficient dosimetric impact to affect the treatment delivery and require complete replanning.

METHODS

This retrospective study includes 55 patients treated for a head and neck cancer with IMRT, VMAT, or 3D conformal RT. Electronic Portal Imaging Device images for all treatment fields were acquired daily at every fraction. CBCTs were collected at least once a week. Gamma analysis was performed using the first fraction of the treatment as a reference once validated that it was delivered without error. Gamma analysis parameters (<γ>, standard deviation and the Top 1% γ) were used to define categories using statistic from a k-means clustering analysis. From these categories an action threshold was defined and correlated with dosimetric changes. For 23 of 55 patients, the V100% for PTV was computed for both, the planning CT and original contours deformed onto CBCT acquired at the last fraction. These values were then compared with 2D image relative γ-analysis of EPID images. Sensitivity and specificity of the method for the detection of dosimetric changes were computed.

RESULTS

Three categories indicating an increasing level of change with the planned treatment were identified. A threshold was established for which patients were at risk of deviation at <γ> = 0.42. From 23 recomputing plans, it has been confirmed that patients with a strong dosimetric impact were above this threshold, with a specificity of 0.80 and a sensitivity of 0.84.

CONCLUSIONS

The specificity and the sensitivity value confirmed the performance of the method to detect anatomical changes. The γ-analysis threshold correlated well with morphological changes that have a relevant dosimetric impact. Analysis of daily EPID images provides a method to identify patients at risk of deviation from their planned treatment and can support an early replanning decision.