Tumor Biology

, Volume 35, Issue 9, pp 8551–8558

Diagnostic value of computed tomography scanning in differentiating malignant from benign solitary pulmonary nodules: a meta-analysis

Research Article

DOI: 10.1007/s13277-014-2113-8

Cite this article as:
Zhang, C., Yu, H., Li, X. et al. Tumor Biol. (2014) 35: 8551. doi:10.1007/s13277-014-2113-8
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Abstract

An early diagnosis of lung cancer is crucial for early treatment and management. The objective of this systematic review was to assess the overall diagnostic accuracy of chest computed tomography (CT) scanning in differentiating malignant from benign solitary pulmonary nodules (SPNs) with meta-analysis. The PubMed and China National Knowledge Infrastructure (CNKI) database were searched for eligible studies published up to March 2014. The sensitivity, specificity, and other measures of accuracy of chest CT scanning in the diagnosis of SPNs were pooled along with 95 % confidence intervals (CI). Summary receiver operating characteristic (ROC) curves were used to summarize overall test performance. Thirty-two studies met our inclusion criteria. The summary estimates for chest CT scanning in the diagnosis of SPNs in the meta-analysis were as follows: pooled sensitivity, 0.89 (95 % CI, 0.88 to 0.91); pooled specificity, 0.70 (95 % CI, 0.68 to 0.73); positive likelihood ratio, 2.88 (95 % CI, 2.46 to 3.37); negative likelihood ratio, 0.16 (95 % CI, 0.12 to 0.21); and diagnostic odds ratio, 23.83 (95 % CI, 16.18 to 35.11). The results indicate that CT scanning has relatively high sensitivity and moderate specificity for the diagnosis of SPNs. Given the low cost and growing prevalence of the technology, CT scanning should be recommended as the initial test for the evaluation of SPNs.

Keywords

Computed tomographySolitary pulmonary nodulesSensitivitySpecificityMeta-analysis

Copyright information

© International Society of Oncology and BioMarkers (ISOBM) 2014

Authors and Affiliations

  • Chuan-yu Zhang
    • 1
  • Hua-long Yu
    • 1
  • Xia Li
    • 2
  • Yong-ye Sun
    • 3
  1. 1.Department of Radiology, Affiliated Hospital of Medical CollegeQingdao UniversityQingdaoPeople’s Republic of China
  2. 2.Department of Pathology, Affiliated Hospital of Medical CollegeQingdao UniversityQingdaoChina
  3. 3.Department of Epidemiology and Health StatisticsMedical College of Qingdao UniversityQingdaoChina