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

Authors Yao Y, Yan C, Zhang W, Wu SG, Guan J, Zeng G, Du Q, Huang C, Zhang H, Wang H, Hou Y, Li Z, Wang L, Zheng Y, Li X

Received 1 June 2019

Accepted for publication 23 August 2019

Published 29 October 2019 Volume 2019:11 Pages 9213—9223

DOI https://doi.org/10.2147/CMAR.S217970

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Amy Norman

Peer reviewer comments 3

Editor who approved publication: Dr Eileen O'Reilly


Yiyong Yao,1,* Cunling Yan,2,* Wei Zhang,3,* San-Gang Wu,4,* Jie Guan,2,* Gang Zeng,1,* Qiang Du,1 Chun Huang,1 Hui Zhang,5 Huiling Wang,6 Yanfeng Hou,2 Zhiyan Li,2 Lixin Wang,7 Yijie Zheng,8 Xun Li9

1Department of Respiratory Medicine, Suzhou Municipal Hospital, Nanjing Medical University, Suzhou, People’s Republic of China; 2Department of Clinical Laboratory, Peking University First Hospital, Beijing, People’s Republic of China; 3Department of Biostatistics, School of Public Health, Fudan University, Shanghai, People’s Republic of China; 4Department of Radiation Oncology, Xiamen Cancer Hospital, The First Affiliated Hospital of Xiamen University, Xiamen, People’s Republic of China; 5Department of Laboratory, Suzhou Municipal Hospital, Nanjing Medical University, Suzhou, People’s Republic of China; 6Department of Respiratory Medicine, The Second Affiliated Hospital, Dalian Medical University, Dalian, People’s Republic of China; 7Department of TCM and Western Medicine, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai, People’s Republic of China; 8Medical Scientific Affairs, Abbott Diagnostics Division, Abbott Laboratories, Asian Pacific Group, Shanghai, People’s Republic of China; 9Department of Laboratory Medicine, The First Affiliated Hospital, School of Medicine, Xiamen University, Xiamen, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yijie Zheng
Medical Scientific Affairs, Abbott Diagnostics Division, Abbott Laboratories, 388 Nanjing Xi Rd, Shanghai 200000, People’s Republic of China
Tel +86 212 315 4961
Fax +86 216 334 6331
Email yijiezheng2015@163.com
Xun Li
Department of Laboratory Medicine, The First Affiliated Hospital, School of Medicine, Xiamen University, 55 Zhenhai Rd, Xiamen, Fujian 361003, People’s Republic of China
Tel + 865922139507
Email xli2001@xmu.edu.cn

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

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