Qiang Du1, Cunling Yan2, San-Gang Wu3, Wei Zhang4, Chun Huang5, Yiyong Yao5, Liyu Wang6, Qunji Zhang7, Qinghao Liu8, Jie Guan2, Yanfeng Hou2, Zhiyan Li2, Andrew Soh9, Agim Beshiri9, Qi Wang10, Xun Li11, Yijie Zheng12, Huiling Wang13. 1. Department of Respiratory Medicine, The Second Affiliated Hospital, Dalian Medical University, Dalian, China; Department of Respiratory Medicine, The North Area of Suzhou Municipal Hospital, Suzhou, China. 2. Department of Clinical Laboratory, Peking University First Hospital, Beijing, China. 3. Department of Radiation Oncology, Xiamen Cancer Hospital, The First Affiliated Hospital of Xiamen University, Xiamen, China. 4. Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China. 5. Department of Respiratory Medicine, The North Area of Suzhou Municipal Hospital, Suzhou, China. 6. Department of Oncology, The North Area of Suzhou Municipal Hospital, Suzhou, China. 7. Department of Laboratory Medicine, The First Affiliated Hospital, Medical College of Xiamen University, Xiamen, Fujian 361003, China. 8. Department of Pulmonary Surgery, Peking University First Hospital, Beijing, China. 9. Medical Scientific Liaison Asian Pacific, Abbott Diagnostics Division, Abbott Laboratories, Asian Pacific Group, China. 10. Department of Respiratory Medicine, The Second Affiliated Hospital, Dalian Medical University, Dalian, China. Electronic address: wqdlmu@163.com. 11. Department of Laboratory Medicine, The First Affiliated Hospital, Medical College of Xiamen University, Xiamen, Fujian 361003, China. Electronic address: xli2001@xmu.edu.cn. 12. Medical Scientific Liaison Asian Pacific, Abbott Diagnostics Division, Abbott Laboratories, Asian Pacific Group, China. Electronic address: yijie.zheng@abbott.com. 13. Department of Respiratory Medicine, The Second Affiliated Hospital, Dalian Medical University, Dalian, China. Electronic address: Whl882728@163.com.
Abstract
PURPOSE: This study aimed to build a valid diagnostic nomogram for assessing the cancer risk of the pulmonary lesions identified by chest CT. PATIENTS AND METHODS: A total of 691 patients with pulmonary lesions were recruited from three centers in China. The cut-off value for each tumor marker was confirmed by minimum P value method with 1000 bootstrap replications. The nomogram was based on the predictive factors identified by univariate and multivariate analysis. The predictive performance of the nomogram was measured by concordance index and calibrated with 1000 bootstrap samples to decrease the overfit bias. We also evaluated the net benefit of the nomogram via decision curve analysis. Finally, the nomogram was validated externally using a separate cohort of 305 patients enrolled from two additional institutions. RESULTS: The cut-off for CEA, SCC, CYFRA21-1, pro-GRP, and HE4 was 4.8 ng/mL, 1.66 ng/mL, 1.83 ng/mL, 56.55 pg/mL, and 63.24Lpmol/L, respectively. Multivariate logistic regression model (LRM) identified tumor size, CEA, SCC, CYFRA21-1, pro-GRP, and HE4 as independent risk factors for lung cancer. The nomogram based on LRM coefficients showed concordance index of 0.901 (95% CI: 0.842-0.960; P < 0.001) for lung cancer in the training set and 0.713 (95% CI: 0.599-0.827; P < 0.001) in the validation set. Decision curve analysis reported a net benefit of 87.6% at 80% threshold probability superior to the baseline model. CONCLUSION: Our diagnostic nomogram provides a useful tool for assessing the cancer risk of pulmonary lesions identified in CT screening test.
PURPOSE: This study aimed to build a valid diagnostic nomogram for assessing the cancer risk of the pulmonary lesions identified by chest CT. PATIENTS AND METHODS: A total of 691 patients with pulmonary lesions were recruited from three centers in China. The cut-off value for each tumor marker was confirmed by minimum P value method with 1000 bootstrap replications. The nomogram was based on the predictive factors identified by univariate and multivariate analysis. The predictive performance of the nomogram was measured by concordance index and calibrated with 1000 bootstrap samples to decrease the overfit bias. We also evaluated the net benefit of the nomogram via decision curve analysis. Finally, the nomogram was validated externally using a separate cohort of 305 patients enrolled from two additional institutions. RESULTS: The cut-off for CEA, SCC, CYFRA21-1, pro-GRP, and HE4 was 4.8 ng/mL, 1.66 ng/mL, 1.83 ng/mL, 56.55 pg/mL, and 63.24Lpmol/L, respectively. Multivariate logistic regression model (LRM) identified tumor size, CEA, SCC, CYFRA21-1, pro-GRP, and HE4 as independent risk factors for lung cancer. The nomogram based on LRM coefficients showed concordance index of 0.901 (95% CI: 0.842-0.960; P < 0.001) for lung cancer in the training set and 0.713 (95% CI: 0.599-0.827; P < 0.001) in the validation set. Decision curve analysis reported a net benefit of 87.6% at 80% threshold probability superior to the baseline model. CONCLUSION: Our diagnostic nomogram provides a useful tool for assessing the cancer risk of pulmonary lesions identified in CT screening test.