Literature DB >> 17478008

Current status of PET/CT for tumour volume definition in radiotherapy treatment planning for non-small cell lung cancer (NSCLC).

Carlo Greco1, Kenneth Rosenzweig, Giuseppe Lucio Cascini, Oscar Tamburrini.   

Abstract

Target volume delineation of lung cancer is well known to be prone to large inter-observer variability. The advent of PET/CT devices, with co-registered functional and anatomical data, has opened new exciting possibilities for target volume definition in radiation oncology. PET/CT imaging is rapidly being embraced by the radiation oncology community as a tool to improve the accuracy of target volume delineation for treatment optimization in NSCLC. Several studies have dealt with the feasibility of incorporating FDG-PET information into contour delineation with the aim to improve overall accuracy and to reduce inter-observer variation. A significant impact of PET-derived contours on treatment planning has been shown in 30-60% of the plans with respect to the CT-only target volume. The most prominent changes in the gross tumour volume (GTV) have been reported in cases with atelectasis and following the incorporation of PET-positive nodes in otherwise CT-insignificant nodal areas. Although inter-observer variability is still present following target volume delineation with PET/CT, it is greatly reduced compared to conventional CT-only contouring. PET/CT may also provide improved therapeutic ratio compared to conventional CT planning. Increased target coverage and often reduced target volumes may potentially result in PET/CT-based planning to yield better tumour control probability through dose escalation, while still complying with dose/volume constrains for normal tissues. Despite these exciting results, more clinical studies need to be performed to better define the role of combined PET/CT in treatment planning for NSCLC.

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Year:  2007        PMID: 17478008     DOI: 10.1016/j.lungcan.2007.03.020

Source DB:  PubMed          Journal:  Lung Cancer        ISSN: 0169-5002            Impact factor:   5.705


  43 in total

Review 1.  PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques.

Authors:  Habib Zaidi; Issam El Naqa
Journal:  Eur J Nucl Med Mol Imaging       Date:  2010-03-25       Impact factor: 9.236

2.  Whole-body metabolic tumour volume of 18F-FDG PET/CT improves the prediction of prognosis in small cell lung cancer.

Authors:  Jong-Ryool Oh; Ji-Hyoung Seo; Ari Chong; Jung-Joon Min; Ho-Chun Song; Young-Chul Kim; Hee-Seung Bom
Journal:  Eur J Nucl Med Mol Imaging       Date:  2012-01-21       Impact factor: 9.236

3.  Broadening the scope of image-guided radiotherapy (IGRT).

Authors:  Carlo Greco; C Clifton Ling
Journal:  Acta Oncol       Date:  2008       Impact factor: 4.089

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

Authors:  Hannah Mary T Thomas; Devadhas Devakumar; Balukrishna Sasidharan; Stephen R Bowen; Danie Kingslin Heck; E James Jebaseelan Samuel
Journal:  J Med Imaging (Bellingham)       Date:  2017-01-23

Review 5.  Predicting outcomes in radiation oncology--multifactorial decision support systems.

Authors:  Philippe Lambin; Ruud G P M van Stiphout; Maud H W Starmans; Emmanuel Rios-Velazquez; Georgi Nalbantov; Hugo J W L Aerts; Erik Roelofs; Wouter van Elmpt; Paul C Boutros; Pierluigi Granone; Vincenzo Valentini; Adrian C Begg; Dirk De Ruysscher; Andre Dekker
Journal:  Nat Rev Clin Oncol       Date:  2012-11-20       Impact factor: 66.675

6.  The potential of positron emission tomography for intratreatment dynamic lung tumor tracking: a phantom study.

Authors:  Jaewon Yang; Tokihiro Yamamoto; Samuel R Mazin; Edward E Graves; Paul J Keall
Journal:  Med Phys       Date:  2014-02       Impact factor: 4.071

7.  Oesophageal squamous cell carcinoma: relationship between fluorine-18 fludeoxyglucose positron emission tomography CT maximum standardised uptake value, metabolic tumour volume, and tumour, node and metastasis classification.

Authors:  W-Q Zhu; X Sun; L Xing; M Li; J Yue; W Qu; X Sun; L Kong; J Yu
Journal:  Br J Radiol       Date:  2012-08       Impact factor: 3.039

8.  Variational PET/CT Tumor Co-segmentation Integrated with PET Restoration.

Authors:  Laquan Li; Wei Lu; Shan Tan
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2019-04-16

9.  Application of machine learning methodology for PET-based definition of lung cancer.

Authors:  A Kerhet; C Small; H Quon; T Riauka; L Schrader; R Greiner; D Yee; A McEwan; W Roa
Journal:  Curr Oncol       Date:  2010-02       Impact factor: 3.677

10.  A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.

Authors:  Emmanuel Rios Velazquez; Hugo J W L Aerts; Yuhua Gu; Dmitry B Goldgof; Dirk De Ruysscher; Andre Dekker; René Korn; Robert J Gillies; Philippe Lambin
Journal:  Radiother Oncol       Date:  2012-11-15       Impact factor: 6.280

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