Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis

Abstract

Abstract Background To date, multiple predictive models have been developed with the goal of reliably differentiating between solitary pulmonary nodules (SPNs) that are malignant and those that are benign. The present meta-analysis was conducted to assess the diagnostic utility of these predictive models in the context of SPN differential diagnosis. Methods The PubMed, Embase, Cochrane Library, CNKI, Wanfang, and VIP databases were searched for relevant studies published through August 31, 2021. Pooled data analyses were conducted using Stata v12.0. Results In total, 20 retrospective studies that included 5171 SPNs (malignant/benign: 3662/1509) were incorporated into this meta-analysis. Respective pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic score values were 88% (95CI%: 0.84–0.91), 78% (95CI%: 0.74–0.80), 3.91 (95CI%: 3.42–4.46), 0.16 (95CI%: 0.12–0.21), and 3.21 (95CI%: 2.87–3.55), with an area under the summary receiver operating characteristic curve value of 86% (95CI%: 0.83–0.89). Significant heterogeneity among studies was detected with respect to sensitivity (I2 = 89.07%), NLR (I2 = 87.29%), and diagnostic score (I2 = 72.28%). In a meta-regression analysis, sensitivity was found to be impacted by the standard reference in a given study (surgery and biopsy vs. surgery only, P = 0.02), while specificity was impacted by whether studies were blinded (yes vs. unclear, P = 0.01). Sensitivity values were higher when surgery and biopsy samples were used as a standard reference, while unclear blinding status was associated with increased specificity. No significant evidence of publication bias was detected for the present meta-analysis (P = 0.539). Conclusions The results of this meta-analysis demonstrate that predictive models can offer significant diagnostic utility when establishing whether SPNs are malignant or benign.

Keywords

• Predictive model
• Diagnosis
• Solitary pulmonary nodule
• Meta-analysis