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Journal of Medical Radiation Sciences
Article . 2022 . Peer-reviewed
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Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions

Authors: Ahmed Allam Mohamed; Kathrin Risse; Laura Schmitz; Marsha Schlenter; Ahmed Chughtai; Maria Ivanciu; Michael J. Eble;

Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions

Abstract

AbstractIntroductionStereotactic body radiotherapy (SBRT) is an ablative method for lung malignancies. Here, the definition of the gross target volume (GTV) is subject to interobserver variation. In this study, we aimed to evaluate the interobserver variability during SBRT and its dosimetric impact, as well as to introduce a semi‐automated delineation tool for both planning computer tomography (P‐CT) and cone beam CT (CBCT) to help to standardise GTV delineation and adaptive volume‐change registration.MethodsThe interobserver variation of GTV manual contours from five physicians was analysed in 15 patients after lung SBRT on free breathing (FB) P‐CT (n = 15) and CBCT (n = 90) before and after each fraction. The dosimetric impact from interobserver variations of GTV based on the original treatment plan was analysed. Next, the accuracy of an in‐house easy‐to‐use semi‐automated‐segmentation algorithm for pulmonary lesions was compared with gold standard contours in FB P‐CT and CBCT, as well as 4D P‐CT of additional 10 patients.ResultsThe interobserver variability in manual contours resulted in violations of dose coverage of the planning target volume (PTV), which, in turn, resulted in compromised tumour control probability in contours from four physicians. The validation of the semi‐automated delineation algorithm using thorax phantom led to a highly reliable accuracy in defining GTVs. Comparing the unsupervised auto‐contours with the gold standard delineation revealed high equal high concordance for FB P‐CT, 4D P‐CT and CBCT, with a DSC of 0.83, 0.76 and 0.8, respectively. The supervised use of the semi‐automated delineation tool improved its accuracy, with DSCs of 0.86, 0.86 and 0.8 for FB P‐CT, 4D P‐CT and CBCT, respectively. The use of the algorithm was associated with a significantly shorter working time. The semi‐automated delineation tool can accurately register volume changes in CBCTs.ConclusionThe segmentation algorithm provides a reliable, standardised and time‐saving alternative for manual delineation in lung SBRT in P‐CT and CBCT.

Keywords

interobserver variability, Lung Neoplasms, intrafractional variation, Radiotherapy Planning, Computer-Assisted, R895-920, CBCT, Automated delineation, Original Articles, Spiral Cone-Beam Computed Tomography, Cone-Beam Computed Tomography, Radiosurgery, Medical physics. Medical radiology. Nuclear medicine, Carcinoma, Non-Small-Cell Lung, Humans, interfractional variation, Lung, Algorithms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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gold