Usefulness of circumference difference for estimating the likelihood of malignancy in small solitary pulmonary nodules on CT

Saito, Hajime; Minamiya, Yoshihiro; Kawai, Hideki; Nagao, Taku; Ito, Manabu; Hosono, Yukiko; Motoyama, Satoru; Hashimoto, Manabu; Ishiyama, Koichi; Ogawa, Jun–ichi

doi:10.1016/j.lungcan.2007.06.018

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…There is no reliable correlation of CT window and level settings to PET SUV, although Hong et al (37) developed a method of correlating SUV to window and level thresholds. 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.…”

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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“…While a fast growth rate usually indicates malignancy, the outcome requires sufficient time for the growth, more CT scans, and accurate image segmentation for the nodule volumes at two or more time points. Although the geometry related features from the CT images have played an important role for the differentiation task [13][14][15][16][17][18][19], texture features have gained great attention for lung CADx [20][21][22][23] in recent years. We summarize these recently reported image features and their performance levels in Appendix, where their used datasets are also included.…”

Section: Introductionmentioning

confidence: 99%

Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules

et al. 2014

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Differentiation of malignant and benign pulmonary nodules is of paramount clinical importance. Texture features of pulmonary nodules in CT images reflect a powerful character of the malignancy in addition to the geometry-related measures. This study first compared three well-known types of two-dimensional (2D) texture features (Haralick, Gabor, and local binary patterns or local binary pattern features) on CADx of lung nodules using the largest public database founded by Lung Image Database Consortium and Image Database Resource Initiative and then investigated extension from 2D to three-dimensional (3D) space. Quantitative comparison measures were made by the well-established support vector machine (SVM) classifier, the area under the receiver operating characteristic curves (AUC) and the p values from hypothesis t tests. While the three feature types showed about 90% differentiation rate, the Haralick features achieved the highest AUC value of 92.70% at an adequate image slice thickness, where a thinner or thicker thickness will deteriorate the performance due to excessive image noise or loss of axial details. Gain was observed when calculating 2D features on all image slices as compared to the single largest slice. The 3D extension revealed potential gain when an optimal number of directions can be found. All the observations from this systematic investigation study on the three feature types can lead to the conclusions that the Haralick feature type is a better choice, the use of the full 3D data is beneficial, and an adequate tradeoff between image thickness and noise is desired for an optimal CADx performance. These conclusions provide a guideline for further research on lung nodule differentiation using CT imaging.

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“…[4][5][6][7] However, at the moment, it is difficult to characterize subcentimeter lung nodules with noninvasive imaging techniques alone. 6,8 Furthermore, the detection of groundglass opacity nodules (GGNs), known to exhibit high malignancy potential, has increased, posing an additional burden on clinicians for tissue confirmation. 9 The other condition necessitating tissue confirmation is the presence of small lung nodules in a patient with a known extrathoracic malignancy.…”

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confidence: 99%

Factors determining successful computed tomography–guided localization of lung nodules

Seo

Lee

Kim

et al. 2012

The Journal of Thoracic and Cardiovascular Surgery

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Usefulness of circumference difference for estimating the likelihood of malignancy in small solitary pulmonary nodules on CT

Cited by 12 publications

References 20 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?

Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules

Factors determining successful computed tomography–guided localization of lung nodules

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Usefulness of circumference difference for estimating the likelihood of malignancy in small solitary pulmonary nodules on CT

Cited by 12 publications

References 20 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?

Texture Feature Analysis for Computer-Aided Diagnosis on Pulmonary Nodules

Factors determining successful computed tomography–guided localization of lung nodules

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Product

Resources

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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?