Diagnostic value of computed tomography scanning in differentiating malignant from benign solitary pulmonary nodules: a meta-analysis

Comparing the diagnostic value of 18F-FDG-PET/CT versus CT for differentiating benign and malignant solitary pulmonary nodules: a meta-analysis

Background

This quantitative meta-analysis was conducted to provide an indirect comparison of the diagnostic value of computed tomography (CT) with positron emission tomography (PET)/CT for differentiating benign and malignant solitary pulmonary nodules (SPNs).

Methods

PubMed, Embase, and the Cochrane Library were searched to identify eligible studies throughout November 2018, which differentiated benign and malignant SPNs using CT or PET/CT. The summary sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR), diagnostic odds ratio (DOR), and area under the receiver operating characteristic curve (AUC) were calculated using bivariate generalized linear mixed model and random-effects model. The diagnostic value of CT with PET/CT was indirectly evaluated using the ratio for diagnostic parameters.

Results

The sensitivity, specificity, PLR, NLR, DOR, and AUC for CT were 0.94 [95% confidence interval (CI): 0.87-0.97], 0.73 (95% CI: 0.64-0.80), 3.45 (95% CI: 2.60-4.58), 0.09 (95% CI: 0.04-0.17), 32.01 (95% CI: 15.10-67.86), and 0.89 (95% CI: 0.86-0.91), respectively. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC for PET/CT were 0.89 (95% CI: 0.85-0.92), 0.78 (95% CI: 0.66-0.86), 3.97 (95% CI: 2.57-6.13), 0.15 (95% CI: 0.10-0.20), 24.04 (95% CI: 12.71-45.48), and 0.91 (95% CI: 0.89-0.94), respectively. No significant differences were observed between CT and PET/CT for sensitivity, specificity, PLR, NLR, DOR, and AUC.

Conclusions

This study used both CT and PET/CT with a moderate-to-high diagnostic value for differentiating benign and malignant SPNs and showed no significant differences in diagnostic parameters between CT and PET/CT.

Computed tomography and pathology evaluation of lung ground-glass opacity

The aim of this study was to investigate the pathogenesis of lung ground-glass opacity (GGO) and the diagnostic value of computed tomography scan for lung GGO. Computed tomography (CT) images of 106 lung GGO cases were analyzed retrospectively, and the type, location, size, structure, boundaries and surrounding lung fields were evaluated. There were 12 cases of GGO with a diameter <1.0 cm, 36 cases with diameter of 1.0-1.5 cm, 25 cases with diameter of 1.6-2.0 cm, 19 cases with diameter of 2.0-2.5 cm and 14 cases with diameter of 2.5-3.0 cm. There were 20 lesions with a round shape and 68 lesions with an oval shape. There were 56 lesions with spinous processes, 18 lesions with air bronchograms and 37 lesions with surrounding pleural indentation. The diagnosis and differential diagnosis of GGO would be improved with combined CT scan and pathology results.

Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography

Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers. In this retrospective study, we aimed to evaluate whether a combination of radiomic texture and shape features from noncontrast CT scans can enable discrimination between granulomas and adenocarcinomas. Our study is composed of CT scans of 195 patients from two institutions, one cohort for training ([Formula: see text]) and the other ([Formula: see text]) for independent validation. A set of 645 three-dimensional texture and 24 shape features were extracted from CT scans in the training cohort. Feature selection was employed to identify the most informative features using this set. The top ranked features were also assessed in terms of their stability and reproducibility across the training and testing cohorts and between scans of different slice thickness. Three different classifiers were constructed using the top ranked features identified from the training set. These classifiers were then validated on the test set and the best classifier (support vector machine) yielded an area under the receiver operating characteristic curve of 77.8%.

Dynamic contrast-enhanced MRI versus 18F-FDG PET/CT: Which is better in differentiation between malignant and benign solitary pulmonary nodules?

Objective

To prospectively compare the discriminative capacity of dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) with that of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) in the differentiation of malignant and benign solitary pulmonary nodules (SPNs).

Methods

Forty-nine patients with SPNs were included in this prospective study. Thirty-two of the patients had malignant SPNs, while the other 17 had benign SPNs. All these patients underwent DCE-MRI and ¹⁸F-FDG PET/CT examinations. The quantitative MRI pharmacokinetic parameters, including the trans-endothelial transfer constant (K^trans), redistribution rate constant (K_ep), and fractional volume (V_e), were calculated using the Extended-Tofts Linear two-compartment model. The ¹⁸F-FDG PET/CT parameter, maximum standardized uptake value (SUV_max), was also measured. Spearman's correlations were calculated between the MRI pharmacokinetic parameters and the SUV_max of each SPN. These parameters were statistically compared between the malignant and benign nodules. Receiver operating characteristic (ROC) analyses were used to compare the diagnostic capability between the DCE-MRI and ¹⁸F-FDG PET/CT indexes.

Results

Positive correlations were found between K^trans and SUV_max, and between K_ep and SUV_max (P<0.05). There were significant differences between the malignant and benign nodules in terms of the K^trans, K_ep and SUV_max values (P<0.05). The areas under the ROC curve (AUC) of K^trans, K_ep and SUV_max between the malignant and benign nodules were 0.909, 0.838 and 0.759, respectively. The sensitivity and specificity in differentiating malignant from benign SPNs were 90.6% and 82.4% for K^trans; 87.5% and 76.5% for K_ep; and 75.0% and 70.6% for SUV_max, respectively. The sensitivity and specificity of K^trans and K_ep were higher than those of SUV_max, but there was no significant difference between them (P>0.05).

Conclusions

DCE-MRI can be used to differentiate between benign and malignant SPNs and has the advantage of being radiation free.