PURPOSE: To perform a meta-analysis to evaluate the diagnostic performance of the diffusion-weighted imaging (DWI) technique in differentiating malignant from benign lung lesions. MATERIALS AND METHODS: Medical and scientific literature databases were searched for studies that assessed the diagnostic performance of DWI in patients suspected of lung cancer who underwent DWI and biopsy. Only studies in the English or Chinese language and published before September 2011 were considered for inclusion. Methodological quality was assessed by the Quality Assessment of Diagnostic Studies (QUADAS) instrument. Homogeneity was explored by the Chi-square test and inconsistency index. Sensitivities (SEN), specificities, predictive values, diagnostic odds ratio (dOR), and areas under the receiver operator characteristic (ROC) curve were calculated. Potential threshold effect was investigated by using Spearman's correlation coefficient. Publication bias analysis was evaluated by Deeks' asymmetry test. RESULTS: Of 33 eligible studies, 11 were included in the meta-analysis, comprising 755 malignant and 294 benign lesions. Heterogeneity was found to have arisen primarily from threshold effect. The data points from the Deeks' funnel plot indicated the presence of publication bias. Methodological quality was moderate. The pooled weighted SEN with corresponding 95% confidence interval (CI) was 0.80 (95% CI: 0.76, 0.83), SPE was 0.93 (95% CI: 0.91, 0.95), positive likelihood ratio was 9.24 (95% CI: 3.58, 23.83), negative likelihood ratio was 0.24 (95% CI: 0.19, 0.29), and dOR was 46.14 (95% CI: 27.56, 77.26). The area under the ROC curve was 0.91 (95% CI: 0.89, 0.93). CONCLUSION: DWI is a noninvasive, nonradiative, and accurate technique for distinguishing between malignant and benign lung lesions. However, large-scale randomized control trials are necessary to assess its clinical value and to establish standards of DWI for measurement, analysis, and cutoff values of diagnosis.
PURPOSE: To perform a meta-analysis to evaluate the diagnostic performance of the diffusion-weighted imaging (DWI) technique in differentiating malignant from benign lung lesions. MATERIALS AND METHODS: Medical and scientific literature databases were searched for studies that assessed the diagnostic performance of DWI in patients suspected of lung cancer who underwent DWI and biopsy. Only studies in the English or Chinese language and published before September 2011 were considered for inclusion. Methodological quality was assessed by the Quality Assessment of Diagnostic Studies (QUADAS) instrument. Homogeneity was explored by the Chi-square test and inconsistency index. Sensitivities (SEN), specificities, predictive values, diagnostic odds ratio (dOR), and areas under the receiver operator characteristic (ROC) curve were calculated. Potential threshold effect was investigated by using Spearman's correlation coefficient. Publication bias analysis was evaluated by Deeks' asymmetry test. RESULTS: Of 33 eligible studies, 11 were included in the meta-analysis, comprising 755 malignant and 294 benign lesions. Heterogeneity was found to have arisen primarily from threshold effect. The data points from the Deeks' funnel plot indicated the presence of publication bias. Methodological quality was moderate. The pooled weighted SEN with corresponding 95% confidence interval (CI) was 0.80 (95% CI: 0.76, 0.83), SPE was 0.93 (95% CI: 0.91, 0.95), positive likelihood ratio was 9.24 (95% CI: 3.58, 23.83), negative likelihood ratio was 0.24 (95% CI: 0.19, 0.29), and dOR was 46.14 (95% CI: 27.56, 77.26). The area under the ROC curve was 0.91 (95% CI: 0.89, 0.93). CONCLUSION: DWI is a noninvasive, nonradiative, and accurate technique for distinguishing between malignant and benign lung lesions. However, large-scale randomized control trials are necessary to assess its clinical value and to establish standards of DWI for measurement, analysis, and cutoff values of diagnosis.
Authors: Michael Meier-Schroers; Rami Homsi; Dirk Skowasch; Jens Buermann; Matthias Zipfel; Hans Heinz Schild; Daniel Thomas Journal: J Cancer Res Clin Oncol Date: 2017-09-20 Impact factor: 4.553
Authors: H Koyama; Y Ohno; M Nishio; D Takenaka; T Yoshikawa; S Matsumoto; S Seki; Y Maniwa; T Ito; Y Nishimura; K Sugimura Journal: Br J Radiol Date: 2014-04-30 Impact factor: 3.039