Introduction: Over the years, multiple models have been developed for the evaluation of pulmonary nodules (PNs). This study aimed to comprehensively investigate clinical models for estimating the malignancy probability in patients with PNs.
Methods: PubMed, EMBASE, Cochrane Library, and Web of Science were searched for studies reporting mathematical models for PN evaluation until March 2020. Eligible models were summarized, and network meta-analysis was performed on externally validated models (PROSPERO database CRD42020154731). The cut-off value of 40% was used to separate patients into high prevalence (HP) and low prevalence (LP), and a subgroup analysis was performed.
Results: A total of 23 original models were proposed in 42 included articles. Age and nodule size were most often used in the models, whereas results of positron emission tomography-computed tomography were used when collected. The Mayo model was validated in 28 studies. The area under the curve values of four most often used models (PKU, Brock, Mayo, VA) were 0.830, 0.785, 0.743, and 0.750, respectively. High-prevalence group (HP) models had better results in HP patients with a pooled sensitivity and specificity of 0.83 (95% confidence interval [CI]: 0.78-0.88) and 0.71 (95% CI: 0.71-0.79), whereas LP models only achieved pooled sensitivity and specificity of 0.70 (95% CI: 0.60-0.79) and 0.70 (95% CI: 0.62-0.77). For LP patients, the pooled sensitivity and specificity decreased from 0.68 (95% CI: 0.57-0.78) and 0.93 (95% CI: 0.87-0.97) to 0.57 (95% CI: 0.21-0.88) and 0.82 (95% CI: 0.65-0.92) when the model changed from LP to HP models. Compared with the clinical models, artificial intelligence-based models have promising preliminary results.
Conclusions: Mathematical models can facilitate the evaluation of lung nodules. Nevertheless, suitable model should be used on appropriate cohorts to achieve an accurate result.
Keywords: Lung cancer; Machine learning; Network meta-analysis; Prediction model; Pulmonary nodules.
© 2022 The Authors.