Solitary Pulmonary Nodules: Dynamic Enhanced Multi–Detector Row CT Study and Comparison with Vascular Endothelial Growth Factor and Microvessel Density

Yi, Chin A; Lee, Kyung Soo; Kim, Eun A; Han, Joungho; Kim, Hojoong; Kwon, O Jung; Jeong, Yeon Joo; Kim, Seonwoo

doi:10.1148/radiol.2331031535

Cited by 279 publications

(224 citation statements)

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“…According to some studies, the malignant form of pulmonary lesions tends to expand substantially more than the benign form [26,27], and perfusion increases proportionally with size [28,29]. However, our technique of CT examination, performed to stage the lung masses, does not allow a specific evaluation of net enhancement.…”

Section: Discussionmentioning

confidence: 99%

“…However, our technique of CT examination, performed to stage the lung masses, does not allow a specific evaluation of net enhancement. In fact, lung masses are usually evaluated with a perfusion or dynamic protocol, which could allow the clinician to correlate net enhancement and histotype [29–31]. However, our subcategories may have been too small to infer any conclusion about this type of parameter.…”

Section: Discussionmentioning

confidence: 99%

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Studio comparativo con esame TC contrastografico e con PET-TC del tumore polmonare non a piccole cellule

et al. 2013

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BackgroundPersonalized cancer therapy remains a challenge. In this context, we attempted to identify correlations between tumour angiogenesis, tumour metabolism and tumour cell type. To this aim, we used single=phase multidetector computed tomography (MDCT) and hybrid positron emission tomography-computed tomography (PET/CT) to determine whether net enhancement and standardized uptake value (SUVmax) were correlated with tumour size and cytology in patients affected by non-small cell lung cancer (NSCLC).Material/MethodsOur study included 38 patients (30 men, 8 women, mean age 70) with a NSCLC measuring between 3 cm and 7 cm, using a 16-slice multidetector CT (Brilliance Philips) and with PET-CT (Biograph 16 Siemens Medical Solutions). The following lesion parameters were evaluated: maximum diameter, medium density before contrast injection (CTpre), medium density after contrast injection (CTpost average), density in the most enhanced part of the lesion after contrast (CTpost max), net enhancement, SUVmax, age, and cytology. Correlation coefficient and p-value were computed for each pair of variables. In addition, correlations were computed for each pair of variables, and for all combinations of tumour types. We focused on subsets of data with more than 10 observations, and with correlation r>0.500 and p<0.05.ResultsA weak correlation (r=0.32; p=0.048) was found between SUVmax and tumour size; the correlation was stronger for masses larger than 31 mm (r=0.4515; p=0.0268). No other correlations were found among the variables examined.ConclusionsOur data may have prognostic significance, and could lead to more appropriate surgical treatment and better treatment outcome.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Studio comparativo con esame TC contrastografico e con PET-TC del tumore polmonare non a piccole cellule

et al. 2013

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“…Tumor-induced angiogenesis is the process by which new blood vessels are formed from existing vessels, a process essential for both tumor growth and metastasis (16). It has been suggested that the increased perfusion of angiogenesis results in increased contrast enhancement (measured by peak and net enhancement) to a magnitude determined by the intensity of angiogenesis (13,17).…”

Section: Discussionmentioning

confidence: 99%

“…Although there is historical precedence for using this protocol, the limited temporal sampling frequency constrains the richness of the time-attenuation characteristics of the nodules. Malignant nodules generally exhibit shorter time of peak enhancement and a higher slope of enhancement, due in part to the higher microvessel density (MVD) (13). A higher sampling frequency early in the contrast injection is needed to capture this enhancement behavior (25).…”

Section: Discussionmentioning

confidence: 99%

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Pulmonary nodule characterization: A comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps

Petkovska

Shah

McNitt-Gray

et al. 2006

European Journal of Radiology

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Purpose-To determine whether conventional nodule densitometry or analysis based on contrast enhancement maps of indeterminate lung nodules imaged with contrast-enhanced CT can distinguish benign from malignant lung nodules.Materials and Method-Thin section, contrast-enhanced CT (baseline, and post-contrast series acquired at 45, 90,180, and 360 seconds) was performed on 29 patients with indeterminate lung nodules (14 benign, 15 malignant). A thoracic radiologist identified the boundary of each nodule using semi-automated contouring to form a 3D region-of-interest (ROI) on each image series. The post-contrast series having the maximum mean enhancement was then volumetrically registered to the baseline series. The two series were subtracted volumetrically and the subtracted voxels were quantized into seven color-coded bins, forming a contrast enhancement map (CEM). Conventional nodule densitometry was performed to obtain the maximum difference in mean enhancement values for each nodule from a circular ROI. Three thoracic radiologists performed visual semi-quantitative analysis of each nodule, scoring each map for (a) magnitude and (b) heterogeneity of enhancement throughout the entire volume of the nodule on a 5-point scale. Receiver operator characteristic (ROC) analysis was conducted on these features to evaluate their diagnostic efficacy. Finally, 14 quantitative texture features were calculated for each map. A statistical analysis was performed to combine the 14 texture features to a single factor. ROC analysis of the derived aggregate factor was done as an indicator of malignancy. All features were analyzed for differences between benign and malignant nodules.Results-Using 15 HU as a threshold, 93% (14/15) of malignant and 79% (11/14) of benign nodules demonstrated enhancement. The ROC curve when higher values of enhancement indicate malignancy was generated and area under the curve (AUC) was 0.76. The visually scored magnitude of enhancement was found to be less effective in distinguishing malignant from benign lesions, with an average AUC of 0.62. The visually scored pattern of enhancement was found to be more effective

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Differentiation of pulmonary sclerosing pneumocytoma from solid malignant pulmonary nodules by radiomic analysis on multiphasic CT

Yin

Fan

et al. 2020

J Applied Clin Med Phys

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Purpose To investigate the diagnostic value and feasibility of radiomics‐based texture analysis in differentiating pulmonary sclerosing pneumocytoma (PSP) from solid malignant pulmonary nodules (SMPN) on single‐ and three‐phase computed tomography (CT) images. Materials and Methods A total of 25 PSP patients and 35 SMPN patients with pathologically confirmed results were retrospectively included in this study. For each patient, the tumor regions were manually labeled in images acquired at the noncontrast phase (NCP), arterial phase (AP), and venous phase (VP). The least absolute shrinkage and selection operator (LASSO) method was used to select the most useful predictive features extracted from the CT images. The predictive models that discriminate PSP from SMPN based on single‐phase CT images (NCP, AP, and VP) or three‐phase CT images (Combined model) were developed and validated through fivefold cross‐validation using a logistic regression classifier. Model performance was evaluated using receiver operating characteristic (ROC) analysis. The predictive performance was also compared between the Combined model and human readers. Results Four, five, and five features were selected from NCP, AP, and VP CT images for the development of radiomic models, respectively. The NCP, AP, and VP models exhibited areas under the curve (AUCs) of 0.748 (95% confidence interval [CI], 0.620–0.852), 0.749 (95% CI, 0.620–0.852), and 0.790 (95% CI, 0.665–0.884) in the validation dataset, respectively. The Combined model based on three‐phase CT images outperformed the NCP, AP, and VP models (all p < 0.05), yielding an AUC of 0.882 (95% CI, 0.773–0.951) in the validation dataset. The Combined model displayed noninferior performance compared to two senior radiologists; however, it outperformed two junior radiologists (p = 0.004 and 0.001, respectively). Conclusion The Combined model based on radiomic features extracted from three‐phase CT images achieved radiologist‐level performance and could be used as promising noninvasive tool to differentiate PSP from SMPN.

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Solitary Pulmonary Nodules: Dynamic Enhanced Multi–Detector Row CT Study and Comparison with Vascular Endothelial Growth Factor and Microvessel Density

Abstract: Dynamic enhancement with multi-detector row CT shows high sensitivity and negative predictive values for diagnosis of malignant nodules but low specificity because of highly enhancing benign nodules. Extent of enhancement reflects underlying nodule angiogenesis.

Cited by 279 publications

References 18 publications

Studio comparativo con esame TC contrastografico e con PET-TC del tumore polmonare non a piccole cellule

Studio comparativo con esame TC contrastografico e con PET-TC del tumore polmonare non a piccole cellule

Pulmonary nodule characterization: A comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps

Differentiation of pulmonary sclerosing pneumocytoma from solid malignant pulmonary nodules by radiomic analysis on multiphasic CT

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