Development of risk prediction models for lung cancer based on tumor markers and radiological signs

Background Accurate prediction of malignancy risk for pulmonary lesions with pleural effusion improves early diagnosis of lung cancer. This study aimed to develop and validate a model to predict lung cancer. Methods Clinical data of 536 patients with pulmonary diseases were collected. The risk factors were identified by regression analysis. Three prediction models were developed. The predictive performances of the models were measured by the area under the curves (AUCs) and calibrated with 1000 bootstrap samples to minimize the over‐fitting bias. The net benefits of the models were evaluated by decision curve analysis. Finally, a separate cohort of 134 patients was used to validate the models externally. Results Seven independent risk factors were identified from 18 clinical variables, which included the pleural fluid carcinoembryonic antigen (CEA), serum cytokeratin‐19 fragment (CYFRA 21‐1), the ratio of CEA in the pleural fluid to serum, extrathoracic cancer history (>5 years), tumor size, vessel convergence, and lobulation. The AUCs of the three models were 0.976, 0.927, and 0.944 in the training set and 0.930, 0.845, and 0.944 in the external set, respectively. The accuracies of the three models were 89.6%, 81.4%, and 88.8%. Model 1 showed the best iteration fit (R2 = 0.84, 0.68, and 0.73) and a higher net benefit on decision curve analysis when compared to the other two models. Conclusion The advantageous model could assess the risk of lung cancer in patients with pleural effusion and act as a useful tool for early identification of lung cancer. Accurate prediction of malignant risk of pulmonary lesions with pleural effusion can improve the early diagnosis of lung cancer. We developed and validated a lung cancer prediction model, with variables including CEA, CYFRA21‐1, the ratio of CEA in the pleural fluid to serum, extrathoracic cancer history (>5 years), and radiological signs. The prediction performance of validation set (AUC = 0.930, 95% CI: 0.884–0.975) of this model is great, and the net benefit of decision curve analysis is also superior. We also developed two other models, one based on tumor markers and one based on imaging signs, and found that the combination of the two was indeed superior to the single in the diagnosis of cancer. Besides, all the parameters of the models are objective, readily available, and need no extra tests. Our advantage model can assess the risk of lung cancer in patients with pleural effusion and provide a useful to