ArticleRadiation oncology (London, England)2013
Automated biological target volume delineation for radiotherapy treatment planning using FDG-PET/CT.
Article in Radiation oncology (London, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- PET Imaging in Vulvar Cancer: A Literature Review of the Current Evidence and Clinical Applications.Cancers · 2026Review
- Prognostic Value ofCancers · 2026Article
- [F18] FDG-PET/CT for manual or semiautomated GTV delineation of the primary tumor for radiation therapy planning in patients with esophageal cancer: is it useful?Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2021Article
- Fully 3D Active Surface with Machine Learning for PET Image Segmentation.Journal of imaging · 2020Article
- Development of a new fully three-dimensional methodology for tumours delineation in functional images.Computers in biology and medicine · 2020Article
- Is there an optimal method for measuring baseline metabolic tumor volume in diffuse large B cell lymphoma?European journal of nuclear medicine and molecular imaging · 2018Article
- Computer-assisted framework for machine-learning-based delineation of GTV regions on datasets of planning CT and PET/CT images.Journal of radiation research · 2017Article
- Image-guided radiation therapy in lymphoma management.Radiation oncology journal · 2015Review
- The use of positron emission tomography/computed tomography imaging in radiation therapy: a phantom study for setting internal target volume of biological target volume.Radiation oncology (London, England) · 2015Article
- Adaptive threshold segmentation of pituitary adenomas from FDG PET images for radiosurgery.Journal of applied clinical medical physics · 2014Article
- Recent advances in radiotherapy for thoracic tumours.Journal of thoracic disease · 2013Review
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7 authors.
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Abstract
backgroundThis study compared manually delineated gross tumour volume (GTV) and automatically generated biological tumour volume (BTV) based on fluoro-deoxy-glucose (FDG) positron emission tomography (PET)/CT to assess the robustness of predefined PET algorithms for radiotherapy (RT) planning in routine clinical practice.
methodsRT-planning data from 20 consecutive patients (lung- (40%), oesophageal- (25%), gynaecological- (25%) and colorectal (10%) cancer) who had undergone FDG-PET/CT planning between 08/2010 and 09/2011 were retrospectively analysed, five of them underwent neoadjuvant chemotherapy before radiotherapy. In addition to manual GTV contouring, automated segmentation algorithms were applied-among these 38%, 42%, 47% and 50% SUVmax as well as the PERCIST total lesion glycolysis (TLG) algorithm. Different ratios were calculated to assess the overlap of GTV and BTV including the conformity index and the ratio GTV included within the BTV.
resultsMedian age of the patients was 66 years and median tumour SUVmax 9.2. Median size of the GTVs defined by the radiation oncologist was 43.7 ml. Median conformity indices were between 30.0-37.8%. The highest amount of BTV within GTV was seen with the 38% SUVmax algorithm (49.0%), the lowest with 50% SUVmax (36.0%). Best agreement was obtained for oesophageal cancer patients with a conformity index of 56.4% and BTV within GTV ratio of 71.1%.
conclusionsAt present there is only low concordance between manually derived GTVs and automatically segmented FDG-PET/CT based BTVs indicating the need for further research in order to achieve higher volumetric conformity and therefore to get access to the full potential of FDG-PET/CT for optimization of radiotherapy planning.
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