Solitary pulmonary nodules: dynamic enhanced multi-detector row CT study and comparison with vascular endothelial growth factor and microvessel density.
OBJECTIVE
To evaluate enhancement dynamics of solitary pulmonary nodules at multi-detector row computed tomography (CT) and to correlate results with extent of tumor angiogenesis in pathologic specimens.


METHODS
One hundred thirty-one patients with solitary pulmonary nodules underwent unenhanced thin-section CT, followed by dynamic helical CT (throughout the nodule for 30 mm along the z-axis [13 images] and at 20-second intervals for 3 minutes [130 images total]) after intravenous injection of 120 mL of contrast medium. Diagnosis of malignancy or benignancy was assigned in 109 patients, and follow-up imaging suggested benignancy in the remaining 22. CT findings were analyzed for peak attenuation, net enhancement, and enhancement dynamics. In 54 patients with surgical diagnoses, Pearson correlation coefficient was used to correlate enhancement pattern with extent of microvessel density and vascular endothelial growth factor (VEGF) staining.


RESULTS
With 30 HU or more of net enhancement as a cutoff value in differentiation of malignant and benign nodules, sensitivity for malignant nodules was 99% (69 of 70 malignant nodules), specificity was 54% (33 of 61 benign nodules), positive predictive value was 71% (69 of 97 malignant readings), negative predictive value was 97% (33 of 34 benign readings), and accuracy was 78% (102 of 131 nodules). Peak attenuation was correlated positively with extent of microvessel density (r = 0.369, P =.006) and VEGF staining (r = 0.277, P =.042). Malignant nodules showed significantly higher VEGF expression (P =.009) than that of benign nodules.


CONCLUSIONS
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.
Department of Radiology and Center for Imaging Science, Samsung Biomedical Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-Dong, Kangnam-Ku, Seoul 135-710, Korea.
Chin A
CA
Yi
Kyung Soo
KS
Lee
Eun A
EA
Kim
Joungho
J
Han
Hojoong
H
Kim
O Jung
OJ
Kwon
Yeon Joo
YJ
Jeong
Seonwoo
S
Kim
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003287:Contrast Media / Q000008:administration & dosage; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007275:Injections, Intravenous; D007479:Iopamidol / Q000008:administration & dosage / Q000031:analogs & derivatives*; D008175:Lung Neoplasms / Q000098:blood supply / Q000000981:diagnostic imaging*; D008297:Male; D008833:Microcirculation / Q000473:pathology; D008875:Middle Aged; D009389:Neovascularization, Pathologic / Q000000981:diagnostic imaging* / Q000473:pathology; D011237:Predictive Value of Tests; D011856:Radiographic Image Enhancement; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000473:pathology; D018709:Statistics, Nonparametric; D036542:Tomography, Spiral Computed / Q000379:methods*; D042461:Vascular Endothelial Growth Factor A / Q000032:analysis*
D003287:Contrast Media; D042461:Vascular Endothelial Growth Factor A; C057937:iomeprol; D007479:Iopamidol