CT-based patient modeling for head and neck hyperthermia treatment planning: Manual versus automatic normal-tissue-segmentation
Radiotherapy & Oncology , Volume 111 - Issue 1 p. 158- 163
Background and purpose Clinical trials have shown that hyperthermia, as adjuvant to radiotherapy and/or chemotherapy, improves treatment of patients with locally advanced or recurrent head and neck (H&N) carcinoma. Hyperthermia treatment planning (HTP) guided H&N hyperthermia is being investigated, which requires patient specific 3D patient models derived from Computed Tomography (CT)-images. To decide whether a recently developed automatic-segmentation algorithm can be introduced in the clinic, we compared the impact of manual- and automatic normal-tissue-segmentation variations on HTP quality. Material and methods CT images of seven patients were segmented automatically and manually by four observers, to study inter-observer and intra-observer geometrical variation. To determine the impact of this variation on HTP quality, HTP was performed using the automatic and manual segmentation of each observer, for each patient. This impact was compared to other sources of patient model uncertainties, i.e. varying gridsizes and dielectric tissue properties. Results Despite geometrical variations, manual and automatic generated 3D patient models resulted in an equal, i.e. 1%, variation in HTP quality. This variation was minor with respect to the total of other sources of patient model uncertainties, i.e. 11.7%. Conclusions Automatically generated 3D patient models can be introduced in the clinic for H&N HTP.
|Automatic segmentation, Dosimetry, Inter-observer variability, Intra-observer variability, Manual segmentation, Sensitivity analysis|
|Radiotherapy & Oncology|
|Organisation||Department of Radiation Oncology|
Verhaart, R.F, Fortunati, E, Verduijn, G.M, van Walsum, T.W, Veenland, J.F, & Paulides, M.M. (2014). CT-based patient modeling for head and neck hyperthermia treatment planning: Manual versus automatic normal-tissue-segmentation. Radiotherapy & Oncology, 111(1), 158–163. doi:10.1016/j.radonc.2014.01.027