Comparison of Five Segmentation Tools for 18F-Fluoro-Deoxy-Glucose–Positron Emission Tomography–Based Target Volume Definition in Head and Neck Cancer

Schinagl, Dominic A.X.; Vogel, Wouter V.; Hoffmann, Aswin L.; Dalen, J. A. van; Oyen, Wim J.G.; Kaanders, Johannes H.A.M.

doi:10.1016/j.ijrobp.2007.07.2333

Cited by 200 publications

(153 citation statements)

References 26 publications

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“…Similar studies comparing different thresholding methods on NSCLC patients [41] and head and neck cancer patients [42][43][44] 111213 have reached the same conclusion. To illustrate the magnitude of differences in outlined PET based GTV contours using some of the methods listed above in section II A, Figure 1 shows an example of various contours on a very same PET image.…”

Section: Pet Should Not Be Used For Gtv Definitionsupporting

confidence: 60%

Towards Biological Target Volumes Definition for Radiotherapy Treatment Planning: Quo Vadis PET/CT?

Dević¹

2013

J Nucl Med Radiat Ther

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IntroductionRadiotherapy treatment planning (RTP) over the last few decades has been based on anatomical targets, namely gross tumor volume (GTV) and from it derived clinical target volume (CTV) as well as the planning target volume (PTV). Owing to its superb spatial reproducibility and the ability to provide the information on electron density (useful for heterogeneity corrections), computed tomography (CT) was, and it still represents, the backbone of modern/high technology radiotherapy treatment planning. The lack of sufficient soft tissue contrast in CT resulted in the incorporation of other imaging methods, such as the magnetic resonance imaging (MRI), into the target definition through the process of image co-registration (sometimes incorrectly termed as image fusion). Whereas the MRI has widely contributed to a better definition of the GTV, [1] which represents an anatomical volume, functional information has not been readily available in the framework of conformal 3D treatment planning and delivery in radiotherapy until recently. In addition, it has been widely accepted that a homogeneous dose delivery to the PTV is the standard of care one should pursue to achieve the best local tumor control.Integration of positron emission tomography (PET) and computed tomography (CT) scanners [2][3][4][5] introduced a new dimension in nuclear medicine by combining functional and anatomical imaging in one machine: the PET/CT scanner. Main advantages of PET/CT scanners are:9 Improved quality of reconstructed PET images with the use of a CT map for attenuation correction of emission PET scans 9 Decrease of about half of the PET acquisition time compared to the previous generation of PET scanners, which used an external radioactive source to acquire transmission scan for attenuation correction. In addition, new faster scintillation materials have also contributed to the shortening of the scanning time 9 The combined medical imaging modality is likely helping management of radiotherapy patients 9 Better understanding of tumor metabolic activity spatial localization by the ability to map the distribution of a specific radiopharmaceutical in co-registered PET/CT images, despite the relatively poor spatial resolution of PET image [6,7].With an increased access to PET/CT information and an apparent appreciation that different sections of the tumor functionally do not behave uniformly, radiation oncologists started to contemplate a change in the traditional concept of uniform dose to the PTV delivery. Instead, a notion of biological targeting and dose painting has come into the play. The first question that comes to our attention is how to define the biological target volumes (BTV) and what do they represent? Ten years after the introduction of the PET/CT scanners into the radiation oncology community, target delineation in radiotherapy treatment planning using FDG-PET/CT scans still has a lot of controversy. The aim of this article is to review the limitations of the current methods for the incorporation of FDG based PET/CT ...

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Section: Pet Should Not Be Used For Gtv Definitionsupporting

confidence: 60%

Towards Biological Target Volumes Definition for Radiotherapy Treatment Planning: Quo Vadis PET/CT?

Dević¹

2013

J Nucl Med Radiat Ther

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“…However, when used together MRI, CT, and PET each can add complimentary data to improve tumor delineation [25]. While most studies examining PET alone based GTVs are usually smaller then their counterpart CT based GTVs, in ∼25% of patients of cases the additional information obtained from PET can increase the GTV compared to CT alone based GTVs [10][11][12][13]15,[26][27][28]. In addition, this number can increase to up to 64% depending on the SUV threshold used [28].…”

Section: Discussionmentioning

confidence: 99%

“…Such areas could be part of a simultaneous integrated boost [32,33]. It is unclear whether these targets for dose escalation should be defined by, visual interpretation [11], a threshold intensity plateau [28], or if the boost dose intensity should be specifically tailored to each imaging voxel intensity (i.e. painting by numbers) [34].…”

Section: Discussionmentioning

confidence: 99%

“…painting by numbers) [34]. At this time the optimal methods for PET volume segmentation [28] and reconstruction algorithms [35] have not been established, however gradient based methods appear more promising when examining gross tumor specimens [36]. In addition, it is not clear if boost should be given to the pretherapy BTV or to the residual uptake after several weeks of therapy [37].…”

Section: Discussionmentioning

confidence: 99%

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Correlation between pretreatment FDG-PET biological target volume and anatomical location of failure after radiation therapy for head and neck cancers

Soto¹,

Kessler²,

Piert³

et al. 2008

Radiotherapy and Oncology

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“…Also, it was suggested that further research is needed to come to a conclusion of an optimal method for PET segmentation. For more studies with efforts to enhance methods of tumor segmentation on PET scans, the reader is encouraged to review these articles 32, 33, 34, 35. In this study, we explored the differences between using graphical‐ and boundary‐based methods in comparison to the manual method for segmenting the cervical tumor volumes on PET scans.…”

Section: Introductionmentioning

confidence: 99%

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Altazi

Zhang

Fernandez

et al. 2017

J Applied Clin Med Phys

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Site‐specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18Flourine–fluorodeoxyglucose (18F‐FDG) PET images for three parameters: manual versus computer‐aided segmentation methods, gray‐level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board‐certified radiation oncologists manually segmented the metabolic tumor volume (MTV1 and MTV2) for each patient. For comparison, we used a graphical‐based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down‐sampled the tumor volumes into three gray‐levels: 32, 64, and 128 from the original gray‐level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D‐reconstruction algorithms: maximum likelihood‐ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning‐ML‐OSEM (FOREIR), FORE‐filtered back projection (FOREFBP), and 3D‐Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray‐levels of down‐sampled volumes, and PET reconstruction algorithms. The features were extracted using gray‐level co‐occurrence matrices (GLCM), gray‐level size zone matrices (GLSZM), gray‐level run‐length matrices (GLRLM), neighborhood gray‐tone difference matrices (NGTDM), shape‐based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV1‐MTV2, MTV1‐GBSV, MTV2‐GBSV; gray‐levels: 64‐32, 64‐128, and 64‐256; reconstruction algorithms: OSEM‐FORE‐OSEM, OSEM‐FOREFBP, and OSEM‐3DRP). We used false|normald¯false| as a measure of radiomic feature reproducibility level, where any feature scored false|normald¯false| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland–Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High— ±1% ≤ U/LRL ≤ ±30%; Intermediate— ±30% < U/LRL ≤ ±45%; Low— ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic fe...

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Comparison of Five Segmentation Tools for 18F-Fluoro-Deoxy-Glucose–Positron Emission Tomography–Based Target Volume Definition in Head and Neck Cancer

Cited by 200 publications

References 26 publications

Towards Biological Target Volumes Definition for Radiotherapy Treatment Planning: Quo Vadis PET/CT?

Towards Biological Target Volumes Definition for Radiotherapy Treatment Planning: Quo Vadis PET/CT?

Correlation between pretreatment FDG-PET biological target volume and anatomical location of failure after radiation therapy for head and neck cancers

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

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