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Lung nodule detection in a high-risk population: comparison of magnetic resonance imaging and low-dose computed tomography

Authors :
Jan Tremper
Heinz Peter Schlemmer
Nikolaus Becker
Gregor Sommer
Jürgen Biederer
Marcel Koenigkam-Santos
Claus Peter Heussel
Hans-Ulrich Kauczor
Stefan Delorme
Michael Puderbach
Source :
European journal of radiology. 83(3)
Publication Year :
2013

Abstract

Objective To investigate the potential of MRI for lung nodule detection in a high-risk population in comparison to low-dose CT. Methods 49 participants (31 men, 18 women, 51–71 years) of the German Lung Cancer Screening and Intervention Trial (LUSI) with a cancer-suspicious lung lesion in CT were examined with non-contrast-enhanced MRI of the lung at 1.5T. Data were pseudonymized and presented at random order together with 30 datasets (23 in men, 7 in women, 18–64 years) from healthy volunteers. Two radiologists read the data for the presence of nodules. Sensitivity and specificity were calculated. Gold standard was either histology or long-term follow-up. Contrast-to-Noise-Ratio (CNR) was measured for all detected lesions in all MRI sequences. Results Average maximum diameter of the lesions was 15mm. Overall sensitivity and specificity of MRI were 48% (26/54) and 88% (29/33) compared to low-dose CT. Sensitivity of MRI was significantly higher for malignant nodules (78% (12.5/16)) than for benign ones (36% (13.5/38); P =0.007). There was no statistically significant difference in sensitivity between nodules (benign and malignant) larger or smaller than 10mm ( P =0.7). Inter observer agreement was 84% ( κ =0.65). Lesion-to-background CNR of T2-weighted single-shot turbo-spin-echo was significantly higher for malignant nodules (89±27) than for benign ones (56±23; P =0.002). Conclusion The sensitivity of MRI for detection of malignant pulmonary nodules in a high-risk population is 78%. Due to its inherent soft tissue contrast, MRI is more sensitive to malignant nodules than to benign ones. MRI may therefore represent a useful test for early detection of lung cancer.

Details

ISSN :
18727727
Volume :
83
Issue :
3
Database :
OpenAIRE
Journal :
European journal of radiology
Accession number :
edsair.doi.dedup.....ff4e538dd640875fdb99e69553e1ef5a