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


METHODS
Thin section, contrast-enhanced CT (baseline, and post-contrast series acquired at 45, 90,180, and 360 s) 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 five-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 with an average AUC of 0.79. From the statistical analysis performed to combine the texture features to a single factor, the area under the ROC curve was 0.84.


CONCLUSIONS
The present study suggests that visual semi-quantitative and quantitative characterization of contrast enhancement patterns may potentially enhance the discrimination between benign and malignant nodules. Further studies and correlation with pathologic material will be important to better understand the potential interplay between CT enhancement features, host stromal elements, and neovascularity that may contribute to these patterns.
Petkovska
Iva
I
Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Thoracic Imaging Research Group, Los Angeles, CA 90095-7319, USA. ipetkovska@mednet.ucla.edu
Shah
Sumit K
SK
McNitt-Gray
Michael F
MF
Goldin
Jonathan G
JG
Brown
Matthew S
MS
Kim
Hyun J
HJ
Brown
Kathleen
K
Aberle
Denise R
DR
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003287:Contrast Media; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011856:Radiographic Image Enhancement / Q000379:methods*; D003074:Solitary Pulmonary Nodule / Q000145:classification / Q000000981:diagnostic imaging*; D015898:Tomography Scanners, X-Ray Computed
D003287:Contrast Media