Forecasting longitudinal changes in oropharyngeal tumor morphology throughout the course of head and neck radiation therapy. Issue 8 (17th July 2014)

• Record Type: Journal Article
• Authors: Yock, Adam D.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Kudchadker, Rajat J.; Court, Laurence E.
• Is Part Of: Medical physics. Volume 41:Issue 8(2014)Part 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-07-17
• Subjects: Therapeutic applications, including brachytherapy; radiation therapy -- statistical analysis -- tumours; morphology -- shape analysis -- modeling -- prediction -- adaptive radiation therapy; Radiation therapy; Cancer -- Radiation treatment -- Anatomy -- Medical imaging -- Computed tomography -- Surface morphology -- Numerical modeling -- Dosimetry; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4887815
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To create models that forecast longitudinal trends in changing tumor morphology and to evaluate and compare their predictive potential throughout the course of radiation therapy. Methods: Two morphology feature vectors were used to describe 35 gross tumor volumes (GTVs) throughout the course of intensity‐modulated radiation therapy for oropharyngeal tumors. The feature vectors comprised the coordinates of the GTV centroids and a description of GTV shape using either interlandmark distances or a spherical harmonic decomposition of these distances. The change in the morphology feature vector observed at 33 time points throughout the course of treatment was described using static, linear, and mean models. Models were adjusted at 0, 1, 2, 3, or 5 different time points (adjustment points) to improve prediction accuracy. The potential of these models to forecast GTV morphology was evaluated using leave‐one‐out cross‐validation, and the accuracy of the models was compared using Wilcoxon signed‐rank tests. Results: Adding a single adjustment point to the static model without any adjustment points decreased the median error in forecasting the position of GTV surface landmarks by the largest amount (1.2 mm). Additional adjustment points further decreased the forecast error by about 0.4 mm each. Selection of the linear model decreased the forecast error for both the distance‐based and spherical harmonic morphology descriptors (0.2 mm), while the mean model decreased the forecast error for the distance‐based descriptor only (0.2 mm). The magnitude and statistical significance of these improvements decreased with each additional adjustment point, and the effect from model selection was not as large as that from adding the initial points. Conclusions: The authors present models that anticipate longitudinal changes in tumor morphology using various models and model adjustment schemes. The accuracy of these models depended on their form, and the utility of these models includes the characterization of patient‐specific response with implications for treatment management and research study design.