%0 Journal Article
%T Differentiation of malignant and benign lung lesions with diffusion-weighted MR imaging
%A Sevtap G¨¹m¨¹ ta
%A Nagihan Inan
%A G¨¹r Akansel
%A Erc¨¹ment  ift i
%A Ali Demirci
%A Sevgiye Ka ar  zkara
%J Radiology and Oncology
%@ 1581-3207
%D 2012
%I 
%R 10.2478/v10019-012-0021-3
%X Background. The aim of the study was to evaluate the role of diffusion-weighted magnetic resonance imaging in the differential diagnosis of lung lesions. Patients and methods. Sixty-seven patients with lung lesions (48 malignant, 19 benign) were included in this prospective study. Signal intensities (SIs) were measured in diffusion-weighted MR images that were obtained with b=0, 500 and 1000 s/mm2 values. Apparent diffusion coefficient (ADC) maps were calculated by using images with b=0 and 1000 s/mm2 values. The statistical significance was determined using the Student-t test. Results. The SIs of malignant lesions were significantly higher than those of benign lesions (p<0.004 for b=0 s/mm2 and p<0.000 for the other b values). Using b=500 s/mm2, SI¡Ý391 indicated a malignant lesion with a sensitivity of 95%, specificity of 73% and positive predictive value of 87%. Using b=1000 s/mm2, SI¡Ý277 indicated a malignant lesion with a sensitivity of 93%, specificity of 69% and positive predictive value of 85%. There was no significant difference between malignant and benign lesions regarding ADC values (p=0.675). There was no significant difference in SIs or ADC values between small cell carcinoma and non-small cell carcinoma. When comparing undifferentiated with well- partially differentiated cancers, SIs were higher with all b values, but the difference was statistically significant only with b=1000 s/mm2 (p<0.04). Conclusions. Diffusion-weighteted MR trace image SI is useful for the differentiation of malignant versus benign lung lesions.
%K pulmonary lesions
%K diffusion-weighted imaging
%K apparent diffusion coefficient
%K magnetic resonance imaging
%U http://versita.metapress.com/content/87wpgv6082760343/?p=621098bd370e4e2bb42fbb80c92fea51&pi=2