Development and validation of a novel diagnostic model for assessing lung cancer metastasis in a Chinese population based on multicenter real-world data

Abstract

Background

Accurate disease staging plays an important role in lung cancer's clinical management. However, due to the limitation of the CT scan, it is still an unmet medical need in practice. In the present study, we attempted to develop diagnostic models based on biomarkers and clinical parameters for assessing lung cancer metastasis.

Methods

This study consisted of 799 patients with pulmonary lesions from three regional centers in China. It included 274 benign lesions patients, 326 primary lung cancer patients without metastasis, and 199 advanced lung cancer patients with lymph node or organ metastasis. The patients were divided into nodules group and masses group according to tumor size.

Results

Four nomogram models based on patient characteristics and tumor biomarkers were developed and evaluated for patients with nodules and masses, respectively. In patients with pulmonary nodules, the AUC to identify metastatic lung cancer from unidentified nodules (including benign nodules and lung cancer, model 1) reached 0.859 (0.827–0.887, 95% CI). Model 2 was used to predict metastasis in patients with lung cancer with AUC of 0.838 (0.795–0.876, 95% CI). In patients with pulmonary masses, the AUC to identify metastatic lung cancer from unidentified masses (model 3) reached 0.773 (0.717–0.823, 95% CI). Model 4 was used to predict metastasis in patients with lung cancer and AUC reached 0.731 (0.771–0.793, 95% CI). Decision curve analysis corroborated good clinical applicability of the nomograms in predicting metastasis.

Conclusion

All new models demonstrated promising discrimination, allowing for estimating the risk of lymph node or organ metastasis of lung cancer. Such integration of blood biomarker testing with CT imaging results will be an efficient and effective approach to benefit the accurate staging and treatment of lung cancer.

Keywords:

• CT imaging pulmonary lesions
• biomarker
• nomogram models
• lung cancer metastasis
• multicenter real-world

Acknowledgments

This work was partly supported by grants from the National Natural Science Foundation of China (81573937 to L.X.W., 81871305 to X.L.), Fujian Natural Science Foundation (2018J01379 to X.L.), Shanghai Science and Technology Fund (15401930900, 18401901500), the Fujian Provincial Health Commission (No. 2018-ZQN-81 to X.L.), Clinical Science and Technology Innovation Project of Shanghai Shenkang Center (SHDC12018X20) and the Science and Technology Planning Projects of Xiamen Science & Technology Bureau (No. 3502Z20174070 to S.W.).

Author contributions

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; gave final approval for the version to be published; and agree to be accountable for all aspects of the work.

Disclosure

Yijie Zheng is an employee of Abbott Diagnostics Division, Abbott Laboratories. The authors report no other conflicts of interest in this work.