Literature DB >> 15850904

Does registration of PET and planning CT images decrease interobserver and intraobserver variation in delineating tumor volumes for non-small-cell lung cancer?

Jana L Fox1, Ramesh Rengan, William O'Meara, Ellen Yorke, Yusuf Erdi, Sadek Nehmeh, Steven A Leibel, Kenneth E Rosenzweig.   

Abstract

PURPOSE: To compare tumor volume delineation using registered positron emission tomography (PET)/CT vs. side-by-side image sets. METHODS AND MATERIALS: A total of 19 patients with non-small-cell lung cancer had 18-fluorine-deoxyglucose (FDG)-PET scans registered with planning CT scans. The disease was Stage I-II in 26%, IIIA in 42%, and IIIB in 32%. Two radiation oncologists contoured 9 tumor volumes using registered images (registered) and 10 using separate FDG-PET images as a guide (nonregistered). A third physician, who had done the treatment planning for these patients a median of 40 months before using registered images, repeated all contours: 10 on registered images (registered/registered) and 9 without registration (registered/nonregistered). Each pair of volumes (A and B) was compared. Quantitative comparison used the concordance index, (A intersection B)/(A union or logical sum B). For qualitative analysis, pairs of volumes were projected onto digitally reconstructed radiographs. The differences were graded as insignificant, minor, moderate, or major.
RESULTS: The median interobserver percentage of concordance among nonregistered pairs was 61% vs. 70% in the registered group (p <0.05). On qualitative analysis, in the nonregistered group, the differences were insignificant in 5, minor in 3, and moderate in 2 of 10. The differences in the registered group were insignificant in 7 and minor in 2 of 9. The median intraobserver percentage of concordance in the registered/nonregistered group was 58% vs. 71% in the registered/registered group (p = 0.10). On qualitative analysis, the intraobserver differences in the registered/nonregistered group were insignificant in 2, minor in 2, moderate in 0, and major in 5 of 9. In the registered/registered group, the differences were insignificant in 2, minor in 6, moderate in 2, and major in 0 of 10.
CONCLUSION: Registration of FDG-PET and planning CT images results in greater consistency in tumor volume delineation.

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Year:  2005        PMID: 15850904     DOI: 10.1016/j.ijrobp.2004.09.020

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  35 in total

1.  Evaluation of the spatial dependence of the point spread function in 2D PET image reconstruction using LOR-OSEM.

Authors:  D Wiant; J A Gersh; M Bennett; J D Bourland
Journal:  Med Phys       Date:  2010-03       Impact factor: 4.071

Review 2.  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

Review 3.  Magnetic resonance imaging in lung: a review of its potential for radiotherapy.

Authors:  Shivani Kumar; Gary Liney; Robba Rai; Lois Holloway; Daniel Moses; Shalini K Vinod
Journal:  Br J Radiol       Date:  2016-02-03       Impact factor: 3.039

4.  From anatomical to biological target volumes: the role of PET in radiation treatment planning.

Authors:  D A X Schinagl; J H A M Kaanders; W J G Oyen
Journal:  Cancer Imaging       Date:  2006-10-31       Impact factor: 3.909

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

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

6.  Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks.

Authors:  Zisha Zhong; Yusung Kim; Kristin Plichta; Bryan G Allen; Leixin Zhou; John Buatti; Xiaodong Wu
Journal:  Med Phys       Date:  2019-01-04       Impact factor: 4.071

7.  Computer-aided diagnosis systems for lung cancer: challenges and methodologies.

Authors:  Ayman El-Baz; Garth M Beache; Georgy Gimel'farb; Kenji Suzuki; Kazunori Okada; Ahmed Elnakib; Ahmed Soliman; Behnoush Abdollahi
Journal:  Int J Biomed Imaging       Date:  2013-01-29

8.  Impact of CT attenuation correction method on quantitative respiratory-correlated (4D) PET/CT imaging.

Authors:  Matthew J Nyflot; Tzu-Cheng Lee; Adam M Alessio; Scott D Wollenweber; Charles W Stearns; Stephen R Bowen; Paul E Kinahan
Journal:  Med Phys       Date:  2015-01       Impact factor: 4.071

9.  Interobserver variation in clinical target volume and organs at risk segmentation in post-parotidectomy radiotherapy: can segmentation protocols help?

Authors:  M Mukesh; R Benson; R Jena; A Hoole; T Roques; C Scrase; C Martin; G A Whitfield; J Gemmill; S Jefferies
Journal:  Br J Radiol       Date:  2012-08       Impact factor: 3.039

Review 10.  A review on segmentation of positron emission tomography images.

Authors:  Brent Foster; Ulas Bagci; Awais Mansoor; Ziyue Xu; Daniel J Mollura
Journal:  Comput Biol Med       Date:  2014-04-28       Impact factor: 4.589

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