Observer variation in contouring gross tumor volume in patients with poorly defined non-small-cell lung tumors on CT: the impact of 18 FDG-hybrid PET fusion

Caldwell, Curtis; Mah, Kandice; Ung, Yee C.; Danjoux, Cyril; Balogh, J; Ganguli, Sujata; Ehrlich, Lisa

doi:10.1016/s0360-3016(01)01722-9

Cited by 297 publications

(142 citation statements)

References 18 publications

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“…The use of variable CT-threshold settings (18,36,38) can undoubtedly affect the GTV. Caldwell et al (39) found a reduction in the ratio of largest to smallest GTV by comparing PET/CT coregistered data with CT alone in a group of 30 patients. Although CT and PET may define a quantitatively similar volume, substantial variations may occur in the qualitative definition of the target, with PET offering valuable metabolic information that could result in enlargements or reductions in the target size.…”

Section: Discussionmentioning

confidence: 99%

Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using ¹⁸F-FDG PET/CT in Non–Small Cell Lung Cancer?

Ung

Hwang

et al. 2010

J Nucl Med

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Previously, we showed that a CT window and level setting of 1,600 and -300 Hounsfield units, respectively, and autocontouring using an 18 F-FDG PET 50% intensity level correlated best with pathologic results. The aim of this study was to compare this autocontouring with manual contouring, to determine which method is better. Methods: Seventeen patients with non-small cell lung cancer underwent 18 F-FDG PET/CT before surgery. The maximum diameter on pathologic examination was determined. Seven sets of gross tumor volumes (GTVs) were defined. The first set (GTV CT ) was contoured manually using only CT information. The second set (GTV Auto ) was autocontoured using a 50% intensity level for 18 F-FDG PET images. The third set (GTV Manual ) was manually contoured using a visual method on PET images. The other 4 sets combined CT and 18 F-FDG PET images fused to one another to become composite volumes: GTV CT1Auto , GTV CT1Manual , GTV CT2Auto , and GTV CT-Manual . To quantitate the degree to which CT and 18 F-FDG PET defined the same region of interest, a matching index was calculated for each case. The maximum diameter of GTV was compared with the maximum diameter on pathologic examination. Results: The median GTV CT , GTV Auto , GTV Manual , GTV CT1Auto , GTV CT1Manual , GTV CT-Auto , and GTV CT-Manual were 6. 96, 2.42, 4.37, 7.46, 10.17, 2.21, and 3.38 cm 3 , respectively. The median matching indexes of GTV CT versus GTV CT1Auto , GTV Auto versus GTV CT1Auto , GTV CT versus GTV CT1Manual , and GTV Manual versus GTV CT1Manual were 0.86, 0.65, 0.88, and 0.81, respectively. Compared with the maximum diameter on pathologic examination, the correlations of GTV CT , GTV Auto , GTV Manual , GTV CT1Auto , and GTV CT1Manual were 0.87, 0.83, 0.93, 0.86, and 0.94, respectively. Conclusion: The matching index was higher for manual contouring than for autocontouring using a 50% intensity level on 18 F-FDG PET images. When using a 50% intensity level to contour the target of non-small cell lung cancer, one should also consider using manual contouring of 18 F-FDG PET to check for any missed disease. It is well established that 18 F-FDG PET plays an important role in the staging of non-small cell lung cancer (NSCLC). Multiple studies have demonstrated the utility of PET for improving staging accuracy. In an overview of the available literature, 18 F-FDG PET was found to have a 79%-100% sensitivity and a 40%-90% specificity in diagnosing primary lung cancer (1).The conventional imaging modality for therapy planning in NSCLC is CT. Targeting of the gross tumor has been facilitated by the use of CT simulation, which allows for more accurate delineation of the tumor. Multimodality imaging combining functional and anatomic information such as PET has allowed for further refinement in the therapy planning process and has a significant impact on the planning target volume. However, much uncertainty exists regarding the most appropriate threshold cutoff that should be used to define a PET target volume in NSCLC therapy planning. There ar...

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Section: Discussionmentioning

confidence: 99%

Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using ¹⁸F-FDG PET/CT in Non–Small Cell Lung Cancer?

Ung

Hwang

et al. 2010

J Nucl Med

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“…PET provides a unique tool for the visualization of biologic processes which can reveal valuable information pertinent to patient diagnosis, staging, progression, and treatment outcome. In oncology, PET data used in conjunction with CT can significantly improve cancer diagnostic accuracy and tumor delineation for radiation treatment planning by providing vital functional information not available otherwise [1][2][3][4][5]. PET data has been shown, for example, to possess greater sensitivity and specificity in the staging of lung cancer [6][7][8] than either CT or MRI alone.…”

Section: Introductionmentioning

confidence: 99%

Properties of Noise in Positron Emission Tomography Images Reconstructed with Filtered-Backprojection and Row-Action Maximum Likelihood Algorithm

et al. 2012

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Noise levels observed in positron emission tomography (PET) images complicate their geometric interpretation. Post-processing techniques aimed at noise reduction may be employed to overcome this problem. The detailed characteristics of the noise affecting PET images are, however, often not well known. Typically, it is assumed that overall the noise may be characterized as Gaussian. Other PET-imaging-related studies have been specifically aimed at the reduction of noise represented by a Poisson or mixed Poisson + Gaussian model. The effectiveness of any approach to noise reduction greatly depends on a proper quantification of the characteristics of the noise present. This work examines the statistical properties of noise in PET images acquired with a GEMINI PET/CT scanner. Noise measurements have been performed with a cylindrical phantom injected with 11 C and well mixed to provide a uniform activity distribution. Images were acquired using standard clinical protocols and reconstructed with filtered-backprojection (FBP) and row-action maximum likelihood algorithm (RAMLA).Statistical properties of the acquired data were evaluated and compared to five noise models (Poisson, normal, negative binomial, log-normal, and gamma). Histograms of the experimental data were used to calculate cumulative distribution functions and produce maximum likelihood estimates for the parameters of the model distributions. Results obtained confirm the poor representation of both RAMLA-and FBP-reconstructed PET data by the Poisson distribution. We demonstrate that the noise in RAMLAreconstructed PET images is very well characterized by gamma distribution followed closely by normal distribution, while FBP produces comparable conformity with both normal and gamma statistics.

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“…Previously reported small studies have shown that FDG-PET/CT modifies the GTV defined by CT [11,[14][15][16][17][18][19][20][21][22][23][24]. Most of these studies determined only the interobserver variability of target volumes for changes in size, not position and overlap.…”

Section: Discussionmentioning

confidence: 99%

“…However based on phase 11 studies, a convincing body of data has emerged with in last ten years incorporating the use of PET scans for radiotherapy planning in NSCLC [7]. The most significant advantage has been the use of integrated FDG-PET and planning CT to reduce interobserver variability of GTV compared to conventional CT planning alone [8][9][10][11]. A review of published data comparing changes in volume measured with FDG-PET/CT to CT alone indicates that the magnitude of treatment volume changes with incorporation of PET in radiotherapy planning for lung cancer varies from 27% -100% [12].…”

Section: Introductionmentioning

confidence: 99%

A Prospective Study to Determine Inter-observer Variability of Gross Tumor Volume with [18F] Fludeoxyglucose-PET/CT Compared to CT Alone in Stage III Non-Small Cell Lung Cancer Using Three-Dimensional Analysis

Peterson¹,

Ahmed²,

Rivest³

et al. 2013

Cureus

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Observer variation in contouring gross tumor volume in patients with poorly defined non-small-cell lung tumors on CT: the impact of 18 FDG-hybrid PET fusion

Cited by 297 publications

References 18 publications

Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using ¹⁸F-FDG PET/CT in Non–Small Cell Lung Cancer?

Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using ¹⁸F-FDG PET/CT in Non–Small Cell Lung Cancer?

Properties of Noise in Positron Emission Tomography Images Reconstructed with Filtered-Backprojection and Row-Action Maximum Likelihood Algorithm

A Prospective Study to Determine Inter-observer Variability of Gross Tumor Volume with [18F] Fludeoxyglucose-PET/CT Compared to CT Alone in Stage III Non-Small Cell Lung Cancer Using Three-Dimensional Analysis

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Observer variation in contouring gross tumor volume in patients with poorly defined non-small-cell lung tumors on CT: the impact of 18 FDG-hybrid PET fusion

Cited by 297 publications

References 18 publications

Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using 18F-FDG PET/CT in Non–Small Cell Lung Cancer?

Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using 18F-FDG PET/CT in Non–Small Cell Lung Cancer?

Properties of Noise in Positron Emission Tomography Images Reconstructed with Filtered-Backprojection and Row-Action Maximum Likelihood Algorithm

A Prospective Study to Determine Inter-observer Variability of Gross Tumor Volume with [18F] Fludeoxyglucose-PET/CT Compared to CT Alone in Stage III Non-Small Cell Lung Cancer Using Three-Dimensional Analysis

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Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using ¹⁸F-FDG PET/CT in Non–Small Cell Lung Cancer?

Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using ¹⁸F-FDG PET/CT in Non–Small Cell Lung Cancer?