Format

Send to

Choose Destination
Eur J Radiol. 2016 Oct;85(10):1765-1772. doi: 10.1016/j.ejrad.2016.07.019. Epub 2016 Jul 28.

Differentiation of benign and malignant lung lesions: Dual-Energy Computed Tomography findings.

Author information

1
Dept Radiophysics, Foundation IVO, Valencia, Spain.
2
Dept Radiology, Foundation IVO, Valencia, Spain. Electronic address: aranae@uv.es.
3
Dept Radiology, Foundation IVO, Valencia, Spain.
4
Dept Pathology, Foundation IVO, Valencia, Spain.
5
GE Healthcare Diagnostic Imaging, Iberia, Spain.
6
Dept Pneumology, Foundation IVO, Valencia, Spain.
7
Dept Thoracic Surgery, Foundation IVO, Valencia, Spain.

Abstract

PURPOSE:

To determine whether parameters generated by Dual-Energy Computed Tomography (DECT) can distinguish malignant from benign lung lesions.

METHODS:

A prospective review of 125 patients with 126 lung lesions (23 benign and 103 malignant) who underwent lung DECT during arterial phase. All lesions were confirmed by tissue sampling. A radiologist semi-automatically contoured lesions and placed regions of interest (ROIs) in paravertebral muscle (PVM) for normalization. Variables related to absorption in Hounsfield units (HU), effective atomic number (Zeff), iodine concentration (ρI) and spectral CT curves were assessed. Receiver operating characteristic (ROC) curves were generated to calculate sensitivity and specificity as predictors of malignancy. Multivariate logistic regression analysis was performed.

RESULTS:

Reproducibility of measures normalized with PVM was poor. Bivariate analysis showed minimum Zeff and normalized mean Zeff to be statistically significant (p=0.001), with area under the curve (AUC) values: 0.66 (CI 95% 0.54-0.80) and 0.72 (CI 95%, 0.60-0.84), respectively. Logistic regression models showed no differences between raw and normalized measurements. In both models, minimum HU (OR: 0.9) and size (OR: 0.1) were predictive of benign lesions.

CONCLUSIONS:

A quantitative approach to DECT using raw measurements is simpler than logistic regression models. Normalization to PVM was not clinically reliable due to its poor reproducibility. Further studies are needed to confirm our findings.

KEYWORDS:

Carcinoma; Computer-assisted; Dual-Energy Multidetector Computed Tomography; Granuloma; Image processing; Lung neoplasms; Non-small-cell lung; Small cell lung carcinoma

PMID:
27666614
DOI:
10.1016/j.ejrad.2016.07.019
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center