An evaluation of an automated 4D-CT contour propagation tool to define an internal gross tumour volume for lung cancer radiotherapy

Gaede, Stewart; Olsthoorn, Jason; Louie, Alexander V.; Palma, David A.; Yu, Edward; Yaremko, Brian; Ahmad, Belal; Chen, Jeff; Bzdusek, Karl; Rodrigues, George

doi:10.1016/j.radonc.2011.08.036

Cited by 37 publications

(40 citation statements)

References 53 publications

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“…Manual delineating on each phase of 4DCT would consume lots of time. Automated 4DCT propagation tools could significantly decrease GTV delineating time, without significantly decreasing the inter- and intra- observer variability [27]. Additionally, it is impossible to avoid the impact of registration error on the comparison of MI or DI between GTV-3D and 4D targets.…”

Section: Discussionmentioning

confidence: 99%

Geometrical differences in gross target volumes between 3DCT and 4DCT imaging in radiotherapy for non-small-cell lung cancer

Zhang

et al. 2013

Journal of Radiation Research

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The aim of this study was to explore the characteristic of 3DCT scanning phases and estimate the comparative amount of respiration motion information included in 3DCT and 4DCT by comparing the volumetric and positional difference between the volumes from 3DCT and 4DCT for the radiotherapy of non-small-cell lung cancer (NSCLC). A total of 28 patients with NSCLC sequentially underwent 3DCT and 4DCT simulation scans of the thorax during free breathing. The 4DCT images with respiratory signal data were reconstructed and sorted into 10 phases throughout a respiratory cycle. GTV-3D from 3DCT, GTV-0%, GTV-20%, GTV-50% and GTV-70% from end-inspiration, mid-expiration, end-expiration and mid-inspiration of 4DCT, and the internal GTV (IGTV-10) from the fused phase of 4DCT were delineated based on the 50% phase image, respectively. The differences in the position, size, matching index (MI) and degree of inclusion (DI) for different volumes were evaluated. The variation in the centroid shifts of GTV-0% and GTV-3D, GTV-20% and GTV-3D, GTV-50% and GTV-3D, and GTV-90% and GTV-3D in the 3D direction was not significant (P = 0.990). The size ratios of GTV-0%, GTV-20%, GTV-50%, GTV-70% and IGTV-10 to GTV-3D were 0.94 ± 0.18, 0.95 ± 0.18, 0.98 ± 0.15, 1.00 ± 0.18 and 1.60 ± 0.55, respectively. DIs of GTV-3D in IGTV-10, and IGTV-10 in GTV-3D were 0.88 ± 0.14 and 0.59 ± 0.16 (P < 0.001). The 3DCT scanning phases are irregular. The CTV-to-ITV expansion should be isotropic when defining the ITV on the 3DCT. The internal GTV derived from 4DCT cannot completely include the GTV from 3DCT. An additional margin may be required when defining the ITV-based 4DCT.

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Section: Discussionmentioning

confidence: 99%

Geometrical differences in gross target volumes between 3DCT and 4DCT imaging in radiotherapy for non-small-cell lung cancer

Zhang

et al. 2013

Journal of Radiation Research

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“…The DSC ranges from 0 (no overlap between T and D ) to 1 ( T and D are identical). A DSC value of greater than 0.7, which denotes a good agreement between T and D , has been generally accepted in the medical field [16–18]. …”

Section: Methodsmentioning

confidence: 99%

Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method

Jin

Arimura

Shioyama

et al. 2014

Journal of Radiation Research

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To assist radiation oncologists in the delineation of tumor regions during treatment planning for lung cancer, we have proposed an automated contouring algorithm based on an optimum contour selection (OCS) method for treatment planning computed tomography (CT) images with positron emission tomography (PET)/CT images. The basic concept of the OCS is to select a global optimum object contour based on multiple active delineations with a level set method around tumors. First, the PET images were registered to the planning CT images by using affine transformation matrices. The initial gross tumor volume (GTV) of each lung tumor was identified by thresholding the PET image at a certain standardized uptake value, and then each initial GTV location was corrected in the region of interest of the planning CT image. Finally, the contours of final GTV regions were determined in the planning CT images by using the OCS. The proposed method was evaluated by testing six cases with a Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs contoured by radiation oncologists and the proposed method. The average three-dimensional DSC for the six cases was 0.78 by the proposed method, but only 0.34 by a conventional method based on a simple level set method. The proposed method may be helpful for treatment planners in contouring the GTV regions.

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“…Surface detection is performed for each triangle center and achieved by maximizing a cost function that evaluates displacements along the triangle normal to deform to a feature based on grey-scale value transitions while simultaneously restricting large displacement vectors (Kaus et al 2004). This deformable model-based method was chosen as it was integrated into the Pinnacle Treatment Planning System 9.1y (Philips Medical Systems, Fitchburg, WI) analogous to the work by Gaede et al (Gaede et al 2011). No auto-segmented volumes in this study were subject to any form of observer review to allow for unbiased assessment of the auto-segmentation accuracy.…”

Section: Auto-segmentationmentioning

confidence: 99%

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

et al. 2015

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This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multiphysician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on autosegmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.

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An evaluation of an automated 4D-CT contour propagation tool to define an internal gross tumour volume for lung cancer radiotherapy

Cited by 37 publications

References 53 publications

Geometrical differences in gross target volumes between 3DCT and 4DCT imaging in radiotherapy for non-small-cell lung cancer

Geometrical differences in gross target volumes between 3DCT and 4DCT imaging in radiotherapy for non-small-cell lung cancer

Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

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