Pulmonary malignant focal ground-glass opacity nodules and solid nodules of 3 cm or less: Comparison of multi-detector CT features

Li, Fan; Liu, Shi-yuan; Li, Qing-Chu; Yu, Hong; Xiang-sheng, Xiao

doi:10.1111/j.1754-9485.2011.02265.x

Cited by 14 publications

(17 citation statements)

References 22 publications

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“…The existing models define spiculation in an “all or none” manner when spiculation is included as a predictor, and false positivity occurs easily. In SPNs with different densities, the frequency of spiculation is also different; spiculation mainly occurs in solid nodules and is relatively rare in ground glass and mixed density nodules . In this study, cases with ground‐glass and mixed density nodules were not removed from the analysis, which is one of the reasons why spiculation was not included in this model.…”

Section: Discussionmentioning

confidence: 99%

A model of malignant risk prediction for solitary pulmonary nodules on ¹⁸F‐FDG PET/CT: Building and estimating

Wang

Yang

et al. 2020

Thoracic Cancer

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Background: To develop a model of malignant risk prediction of solitary pulmonary nodules (SPNs) using metabolic characteristics of lesions. Methods: A total of 362 patients who underwent PET/CT imaging from January 2013 to July 2017 were analyzed. Differences in the clinical and imaging characteristics were analyzed between patients with benign SPNs and those with malignant SPNs. Risk factors were screened by multivariate nonconditional logistic regression analysis. The self-verification of the model was performed by receiver operating characteristic (ROC) curve analysis, and out-of-group verification was performed by k-fold cross-validation. Results: There were statistically significant differences in age, maximum standardized uptake value (SUV max ), size, lobulation, spiculation, pleural traction, vessel connection, calcification, presence of vacuoles, and emphysema between patients with benign nodules and those with malignant nodules (all P < 0.05). The risk factors for malignant nodules included age, SUV max , size, lobulation, calcification and vacuoles. The logistic regression model was as follows: P = l/(1 + e -x ), x = − 5.583 + 0.039 × age + 0.477 × SUV max + 0.139 × size + 1.537 × lobulation -1.532 × calcification + 1.113 × vacuole. The estimated area under the curve (AUC) for the model was 0.915 (95% CI: 0.883-0.947), the sensitivity was 89.7%, and the specificity was 78.9%. K-fold cross-validation showed that the training accuracy was 0.899 AE 0.011, and the predictive accuracy was 0.873 AE 0.053. Conclusions: The risk factors for malignant nodules included age, SUV max , size, lobulation, calcification and vacuoles. After verification, the model has satisfactory accuracy, and it may assist clinics make appropriate treatment decisions.

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Section: Discussionmentioning

confidence: 99%

A model of malignant risk prediction for solitary pulmonary nodules on ¹⁸F‐FDG PET/CT: Building and estimating

Wang

Yang

et al. 2020

Thoracic Cancer

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“…If a GGN develops into a malignancy, it generates more thickened alveolar walls, enlarged alveolar spaces, and dilated bronchioles. 19 Consequently, the GGN becomes more heterogeneous in terms of CT attenuation in the non-solid component. Peng et al reported that the degree of heterogeneity can be identified by the CT density deviation of the non-solid component.…”

Section: Discussionmentioning

confidence: 99%

“…Soda et al and Min et al 22,23 found that a pGGN might evolve stepwise into an IA during long-term follow-up. 18,19 Other studies found that the CT attenuation of the non-solid part varied among different pathological categories. 14,15 A higher CT attenuation value was found to be associated with a higher malignant rate.…”

Section: Discussionmentioning

confidence: 99%

Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons

et al. 2018

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“…Vacuole sign is defined as round or irregular air attenuation with 1–2 mm in diameter in a nodule. 19 Spinous process sign is defined as nodule edges protruded sharply with the appearance of a small triangle. 20 Smooth margin is defined as the margin of the nodule is even without any short or long spiculation.…”

Section: Methodsmentioning

confidence: 99%

Clinical and Computed Tomography Characteristics for Early Diagnosis of Peripheral Small-cell Lung Cancer

Zhang¹,

Lv²,

Fu³

et al. 2022

CMAR

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To investigate the clinical and computed tomography (CT) characteristics of peripheral small-cell lung cancer (pSCLC) to improve its early diagnosis. Patients and Methods: In total, 70, 132, 69, and 95 patients with pathological confirmed nodular (≤3 cm) pSCLC, peripheral nonsmall cell lung cancer (pNSCLC), benign lung tumor (pBLT), and inflammatory lesion (pIL) were enrolled in this study retrospectively. The clinical and CT data of studied patients with different lesions were analyzed and compared by univariate analysis. Multivariate analysis was used to reveal the key features to distinguish pSCLC from pNSCLC, pBLT, and pIL, respectively. Results: Univariate and multivariate analysis of the clinical and CT characteristics of studied patients indicated that 1) compared with pNSCLC and pIL, vessel convergence, spiculation, and peripheral ground-glass opacity were less common in pSCLC; 2) density homogeneity (OR = 38.84-120.21, P < 0.05), bronchial cutoff sign (OR = 10.00-60.13, P = 0.001), hilar lymph node enlargement (OR = 22.81-95.08, P < 0.0001) (pSCLC vs pNSCLC, pBLT, and pIL), male sex (OR = 5.53-10.92, P < 0.05) (pSCLC vs pNSCLC and pBLT), and emphysema (OR = 36.57-56.63, P < 0.05) (pSCLC vs pBLT and pIL) were significantly and independently associated with pSCLC. Type I and II bronchial cutoff sign, especially type I, were closely related to pSCLC. Conclusion: Peripheral solid nodules with homogeneous density, bronchial cutoff sign, hilar lymph node enlargement, but without spiculation or vessel convergence in male patients with emphysema should be highly suspected of pSCLC.

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Pulmonary malignant focal ground-glass opacity nodules and solid nodules of 3 cm or less: Comparison of multi-detector CT features

Abstract: Malignant fGGOs and solid nodules showed mostly similar MDCT features. For malignant fGGOs, the frequency of irregular shape, vacuole sign and natural air bronchograms was higher than that in solid nodules, but the frequency of spiculation was lower than that in solid nodules.

Cited by 14 publications

References 22 publications

A model of malignant risk prediction for solitary pulmonary nodules on ¹⁸F‐FDG PET/CT: Building and estimating

A model of malignant risk prediction for solitary pulmonary nodules on ¹⁸F‐FDG PET/CT: Building and estimating

Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons

Clinical and Computed Tomography Characteristics for Early Diagnosis of Peripheral Small-cell Lung Cancer

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Pulmonary malignant focal ground-glass opacity nodules and solid nodules of 3 cm or less: Comparison of multi-detector CT features

Abstract: Malignant fGGOs and solid nodules showed mostly similar MDCT features. For malignant fGGOs, the frequency of irregular shape, vacuole sign and natural air bronchograms was higher than that in solid nodules, but the frequency of spiculation was lower than that in solid nodules.

Cited by 14 publications

References 22 publications

A model of malignant risk prediction for solitary pulmonary nodules on 18F‐FDG PET/CT: Building and estimating

A model of malignant risk prediction for solitary pulmonary nodules on 18F‐FDG PET/CT: Building and estimating

Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons

Clinical and Computed Tomography Characteristics for Early Diagnosis of Peripheral Small-cell Lung Cancer

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A model of malignant risk prediction for solitary pulmonary nodules on ¹⁸F‐FDG PET/CT: Building and estimating

A model of malignant risk prediction for solitary pulmonary nodules on ¹⁸F‐FDG PET/CT: Building and estimating