Evaluation of Different Methods of 18F-FDG-PET Target Volume Delineation in the Radiotherapy of Head and Neck Cancer

Greco, Carlo; Nehmeh, Sadek; Schöder, Heiko; Gönen, Mithat; Raphael, Barbara; Stambuk, Hilda E.; Humm, John L.; Larson, Steven M.; Lee, Nancy Y.

doi:10.1097/coc.0b013e318168ef82

Cited by 37 publications

(22 citation statements)

References 30 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Intuitively, since FDG PET images show substantial contrast in glucose utilization between normal tissue and neoplastic tissue, threshold-based segmentation is one of the easier methods to adopt and implement. 20,28,[30][31][32] However, most threshold methods (T 25 and T 40 ) fail to identify a single threshold that includes the entire tumor in the presence of pronounced heterogeneities. 7 To overcome this methodological shortcoming, we used the interactive GrowCut algorithm implemented in the 3D Slicer.…”

Section: Discussionmentioning

confidence: 99%

Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization

et al. 2017

View full text Add to dashboard Cite

Abstract. This paper presents an improved GrowCut (IGC), a positron emission tomography-based segmentation algorithm, and tests its clinical applicability. Contrary to the traditional method that requires the user to provide the initial seeds, the IGC algorithm starts with a threshold-based estimate of the tumor and a threedimensional morphologically grown shell around the tumor as the foreground and background seeds, respectively. The repeatability of IGC from the same observer at multiple time points was compared with the traditional GrowCut algorithm. The algorithm was tested in 11 nonsmall cell lung cancer lesions and validated against the clinician-defined manual contour and compared against the clinically used 25% of the maximum standardized uptake value [SUV-(max)], 40% SUV max , and adaptive threshold methods. The time to edit IGC-defined functional volume to arrive at the gross tumor volume (GTV) was compared with that of manual contouring. The repeatability of the IGC algorithm was very high compared with the traditional GrowCut (p ¼ 0.003) and demonstrated higher agreement with the manual contour with respect to threshold-based methods. Compared with manual contouring, editing the IGC achieved the GTV in significantly less time (p ¼ 0.11). The IGC algorithm offers a highly repeatable functional volume and serves as an effective initial guess that can well minimize the time spent on labor-intensive manual contouring.

show abstract

Section: Discussionmentioning

confidence: 99%

Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization

et al. 2017

View full text Add to dashboard Cite

show abstract

“…[24][25][26][27]33,46 This is especially true in the context of evaluating FDG-PET segmentation methods, where the task of interest is the estimation of volumetric metrics such as MTV and TLG. 33,47,48 However, an issue with these taskbased evaluations is the lack of knowledge about the true value of the metric. In this manuscript, the use of the NGS framework to address this issue in evaluating FDG-PET segmentation methods has been demonstrated.…”

Section: Discussionmentioning

confidence: 99%

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

et al. 2017

View full text Add to dashboard Cite

Abstract. Recently, a class of no-gold-standard (NGS) techniques have been proposed to evaluate quantitative imaging methods using patient data. These techniques provide figures of merit (FoMs) quantifying the precision of the estimated quantitative value without requiring repeated measurements and without requiring a gold standard. However, applying these techniques to patient data presents several practical difficulties including assessing the underlying assumptions, accounting for patient-sampling-related uncertainty, and assessing the reliability of the estimated FoMs. To address these issues, we propose statistical tests that provide confidence in the underlying assumptions and in the reliability of the estimated FoMs. Furthermore, the NGS technique is integrated within a bootstrap-based methodology to account for patient-sampling-related uncertainty. The developed NGS framework was applied to evaluate four methods for segmenting lesions from F-Fluoro-2-deoxyglucose positron emission tomography images of patients with head-and-neck cancer on the task of precisely measuring the metabolic tumor volume. The NGS technique consistently predicted the same segmentation method as the most precise method. The proposed framework provided confidence in these results, even when gold-standard data were not available. The bootstrap-based methodology indicated improved performance of the NGS technique with larger numbers of patient studies, as was expected, and yielded consistent results as long as data from more than 80 lesions were available for the analysis.

show abstract

“…Most of the attempts so far have shown a large variability of tumour delineation based on different imaging techniques [34,35], but have provided little guidance on how to effectively reach beyond CT-based tumour volume definition. Part of the reason for this lies in the fact that methodologies for extraction of tumour volumes from other imaging modalities are not satisfactory and have led to large inherent uncertainties between different techniques [36][37][38]. Clearly more work needs to be done, first to better understand the pathological extent of tumours and its relation to different types of imaging; and second, to better explore ways to make imaging more quantitative than it is currently.…”

Section: Target Definition Incorporating Biological Imagingmentioning

confidence: 99%

The physical basis and future of radiation therapy

Bortfeld

Jeraj²

2011

BJR

View full text Add to dashboard Cite

ABSTRACT. The remarkable progress in radiation therapy over the last century has been largely due to our ability to more effectively focus and deliver radiation to the tumour target volume. Physics discoveries and technology inventions have been an important driving force behind this progress. However, there is still plenty of room left for future improvements through physics, for example image guidance and four-dimensional motion management and particle therapy, as well as increased efficiency of more compact and cheaper technologies. Bigger challenges lie ahead of physicists in radiation therapy beyond the dose localisation problem, for example in the areas of biological target definition, improved modelling for normal tissues and tumours, advanced multicriteria and robust optimisation, and continuous incorporation of advanced technologies such as molecular imaging. The success of physics in radiation therapy has been based on the continued ''fuelling'' of the field with new discoveries and inventions from physics research. A key to the success has been the application of the rigorous scientific method. In spite of the importance of physics research for radiation therapy, too few physicists are currently involved in cutting-edge research. The increased emphasis on more ''professionalism'' in medical physics will tip the situation even more off balance. To prevent this from happening, we argue that medical physics needs more research positions, and more and better academic programmes. Only with more emphasis on medical physics research will the future of radiation therapy and other physics-related medical specialties look as bright as the past, and medical physics will maintain a status as one of the most exciting fields of applied physics.

show abstract

Evaluation of Different Methods of 18F-FDG-PET Target Volume Delineation in the Radiotherapy of Head and Neck Cancer

Cited by 37 publications

References 30 publications

Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization

Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

The physical basis and future of radiation therapy

Contact Info

Product

Resources

About