TY - JOUR
T1 - CT-based patient modeling for head and neck hyperthermia treatment planning
T2 - manual versus automatic normal-tissue-segmentation
AU - Verhaart, René F.
AU - Fortunati, Valerio
AU - Verduijn, Gerda M.
AU - van Walsum, Theo
AU - Veenland, Jifke F.
AU - Paulides, Margarethus M.
N1 - Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
PY - 2014/4
Y1 - 2014/4
N2 - 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.
AB - 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.
KW - Algorithms
KW - Carcinoma, Squamous Cell/pathology
KW - Head and Neck Neoplasms/pathology
KW - Humans
KW - Hyperthermia, Induced/methods
KW - Observer Variation
KW - Patient Care Planning
KW - Tomography, X-Ray Computed/methods
U2 - 10.1016/j.radonc.2014.01.027
DO - 10.1016/j.radonc.2014.01.027
M3 - Article
C2 - 24631148
SN - 0167-8140
VL - 111
SP - 158
EP - 163
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
IS - 1
ER -