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11 May 2019 : Meta-Analysis  

Diagnostic Performance of Perfusion Computed Tomography for Differentiating Lung Cancer from Benign Lesions: A Meta-Analysis

Cuiqing Huang12DE, Jianye Liang1BF, Xueping Lei3DF, Xi Xu1B, Zeyu Xiao1AC, Liangping Luo1A*

DOI: 10.12659/MSM.914206

Med Sci Monit 2019; 25:3485-3494


BACKGROUND: Numerous studies have explored diagnosis of pulmonary nodules using perfusion computed tomography (CT); however, findings were not always consistent between studies. Th e present study aimed to summarize evidence on the diagnostic value of perfusion CT for distinguishing between lung cancer and benign lesions.

MATERIAL AND METHODS: We performed a systematic literature search on lung cancer and benign pulmonary lesions performed with perfusion CT. The searches were undertaken in English or Chinese language in Medline, PubMed, Embase, Cochrane Library, Web of Science, and China National Knowledge Infrastructure database from Jan 2010 to Nov 2018. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) of blood volume (BV), blood flow (BF), mean transit time (MTT), and permeability surface (PS) were calculated using Review Manager 5.3. Publication bias, sensitivity, specificity, and the area under the curve (AUC) were calculated using Stata12.0.

RESULTS: Fourteen studies comprising 1032 malignant and 447 benign pulmonary lesions were analyzed. Lung cancer had higher BV, BF, MTT, and PS values than benign lesions. SMDs and 95% CIs of BV, BF, MTT, and PS were 2.29 (1.43, 3.16), 0.50 (0.14, 0.86), 0.55 (0.39, 0.72), and 1.21 (0.87, 1.56), respectively. AUC values of BV and PS were 0.92 (0.90, 0.94) and 0.83 (0.80, 0.86), respectively.

CONCLUSIONS: CT perfusion imaging is a valuable technique for the diagnosis of pulmonary nodules. Lung cancer had higher perfusion and permeability than benign lesions. The evidence suggests blood volume is the best surrogate marker for characterizing the blood supply, while permeability surface has a high specificity in quantifying the vascular permeability.

Keywords: Blood Volume, perfusion imaging, Permeability, Tomography, X-Ray, Aged, Aged, 80 and over, Area Under Curve, Lung, Middle Aged, Perfusion, Publication Bias, Sensitivity and Specificity, Tomography, X-Ray Computed

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Dinah V. Parums

DOI: 10.12659/MSM.943312

Med Sci Monit 2023; 29:e943312


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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750