Abstract
The aim of this study was to quantify the geometrical differences between manual contours
and autocontours, the dosimetric impacts, and the time gain of using autosegmentation
in adaptive nasopharyngeal carcinoma (NPC) intensity-modulated radiotherapy (IMRT)
for a commercial system. A total of 20 consecutive Stages I to III NPC patients who
had undergone adaptive radiation therapy (ART) re planning for IMRT treatment were
retrospectively studied. Manually delineated organs at risks (OARs) on the replanning
computed tomography (CT) were compared with the autocontours generated by VelocityAI
using deformable registration from the original planning CT. Dice similarity coefficients
and distance-to-agreements (DTAs) were used to quantify their geometric differences.
IMRT test plans were generated with the assistance of RapidPlan based on the autocontours
of OARs and manually segmented target volumes. The dose distributions were applied
on the manually delineated OARs, their dose volume histograms and dose constraints
compliances were analyzed. Times spent on target, OAR contouring, and IMRT replanning
were recorded, and the total time of replanning using manual contouring and autocontouring
were compared. The averaged mean DTA of all structures included in the study were
less than 2 mm, and 90% of the patients fulfilled the mean distance agreement tolerance
recommended by AAPM 132.1 The averaged maximum DTA for brainstem, cord, optic chiasm, and optic nerves were
all less than 4 mm, whereas temporal lobes and parotids have larger average maximum
DTA of 4.7 mm and 6.8 mm, respectively. It was found that large contour discrepancies
in temporal lobes and parotids were often associated with large magnitude of deformation
(warp distance) in image registrations. The resultant maximum dose of manually segmented
brainstem, cord, and temporal lobe and the median dose of manually segmented parotids
were found to be statistically higher than those to their autocontoured counter parts
in test plans. Dose constraints of the manually segmented OARs in test plans were
only met in 15% of the cases. The average time of manual contouring and autocontouring
were 108 and 10 minutes, respectively (p < 0.001). More than 30% of the total replanning time would be spent in manual OAR
contouring. Manual OAR delineation takes up a significant portion of time spent in
ART replanning and OAR autocontouring could considerably enhance ART workflow efficiency.
Geometrical discrepancies between auto- and manual contours in head and neck OARs
were comparable to typical interobserver variation suggested in various literatures;
however, some of the corresponding dosimetric differences were substantial, making
it essential to carefully review the autocontours.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic and PersonalCorporate R&D ProfessionalsOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Medical DosimetryAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Clinical outcomes and patterns of failure after intensity-modulated radiotherapy for nasopharyngeal carcinoma.Int J Radiat Oncol Biol Phys. 2011; https://doi.org/10.1016/j.ijrobp.2009.11.024
- Intensity-modulated radiation therapy for head and neck carcinoma.Oncologist. 2007; https://doi.org/10.1634/theoncologist.12-5-555
- Identifying patients who may benefit from adaptive radiotherapy: does the literature on anatomic and dosimetric changes in head and neck organs at risk during radiotherapy provide information to help?.Radiother Oncol. 2015; https://doi.org/10.1016/j.radonc.2015.05.018
- The role of replanning in fractionated intensity modulated radiotherapy for nasopharyngeal carcinoma.Radiother Oncol. 2011; https://doi.org/10.1016/j.radonc.2010.10.009
- A prospective study on volumetric and dosimetric changes during intensity-modulated radiotherapy for nasopharyngeal carcinoma patients.Radiother Oncol. 2012; https://doi.org/10.1016/j.radonc.2012.03.013
- Automatic delineation for replanning in nasopharynx radiotherapy: what is the agreement among experts to be considered as benchmark?.Acta Oncol (Madr). 2013; https://doi.org/10.3109/0284186X.2013.813069
- CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG oncology and TROG consensus guidelines.Radiother Oncol. 2015; https://doi.org/10.1016/j.radonc.2015.07.041
- Evaluation of various deformable image registration algorithms for thoracic images.J Radiat Res. 2014; https://doi.org/10.1093/jrr/rrt093
- Bidirectional local distance measure for comparing segmentations.Med Phys. 2012; https://doi.org/10.1118/1.4754802
- Use of image registration and fusion algorithms and techniques in radiotherapy: report of the AAPM Radiation Therapy Committee Task Group no. 132: report.Med Phys. 2017; https://doi.org/10.1002/mp.12256
- Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation.IEEE Trans Med Imaging. 2004; https://doi.org/10.1109/TMI.2004.828354
- A comparison of ground truth estimation methods.Int J Comput Assist Radiol Surg. 2010; https://doi.org/10.1007/s11548-009-0401-3
- Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study.Phys Med Biol. 2011; https://doi.org/10.1088/0031-9155/56/14/021
- Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer.Int J Radiat Oncol Biol Phys. 2012; https://doi.org/10.1016/j.ijrobp.2010.10.019
- A system for continual quality improvement of normal tissue delineation for radiation therapy treatment planning.Int J Radiat Oncol Biol Phys. 2012; https://doi.org/10.1016/j.ijrobp.2012.02.003
- Recommendation for a contouring method and atlas of organs at risk in nasopharyngeal carcinoma patients receiving intensity-modulated radiotherapy.Radiother Oncol. 2014; https://doi.org/10.1016/j.radonc.2013.10.035
- Comparison of planning quality and efficiency between conventional and knowledge-based algorithms in nasopharyngeal cancer patients using intensity modulated radiation therapy.Int J Radiat Oncol. 2016; https://doi.org/10.1016/j.ijrobp.2016.02.017
- Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer.Radiat Oncol. 2012; https://doi.org/10.1186/1748-717X-7-160
- Survey on deep learning for radiotherapy.Comput Biol Med. 2018; https://doi.org/10.1016/j.compbiomed.2018.05.018
- Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer.Radiother Oncol. 2018; https://doi.org/10.1016/j.radonc.2017.11.012
- Fully automated simultaneous integrated boosted-intensity modulated radiation therapy treatment planning is feasible for head-and-neck cancer: a prospective clinical study.Int J Radiat Oncol Biol Phys. 2012; https://doi.org/10.1016/j.ijrobp.2012.06.047
- Automation of lung treatment planning with the eclipse scripting application programming interface.Int J Radiat Oncol. 2012; https://doi.org/10.1016/j.ijrobp.2012.07.1572
Article Info
Publication History
Published online: July 23, 2019
Accepted:
June 3,
2019
Received in revised form:
April 4,
2019
Received:
December 7,
2018
Identification
Copyright
© 2019 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.
