Development of a nomogram for preoperative prediction of lymph node metastasis in non-small cell lung cancer: a SEER-based study

Chufan Zhang; Qian Song; Lanlin Zhang; Xianghua Wu

doi:10.21037/jtd-20-601

Development of a nomogram for preoperative prediction of lymph node metastasis in non-small cell lung cancer: a SEER-based study

J Thorac Dis. 2020 Jul;12(7):3651-3662. doi: 10.21037/jtd-20-601.

Authors

Chufan Zhang^{1

2}, Qian Song^{1

2

3}, Lanlin Zhang^{1

2}, Xianghua Wu^{1

2}

Affiliations

¹ Departmemt of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
³ Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China.

Abstract

Background: Lymph node dissection is an important part of lung cancer surgery. Preoperational evaluation of lymph node metastases decides which dissection pattern should be chosen. The present study aimed to develop a nomogram to predict lymph node metastases on the basis of clinicopathological features of non-small cell lung cancer (NSCLC) patients.

Methods: A total of 35,138 patients diagnosed with NSCLC from 2010-2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into training cohort and validation cohort. Possible risk factors were included and analyzed by logistic regression models. A nomogram was then constructed and validated.

Results: 21.83% of all patients were confirmed with positive lymph node metastasis. Age at diagnosis, sex, stage, T status, tumor size, grade and laterality were identified as predicting factors for lymph node involvement. These variables were included to build the nomogram. The AUC of the model was 0.696 (95% CI, 0.617 to 0.775). The model was further validated in the validation set with AUC 0.693 (95% CI, 0.628 to 0.758). The model presented with good prediction accuracy in both training cohort and validation cohort.

Conclusions: We developed a convenient clinical prediction model for regional lymph node metastases in NSCLC patients. The nomogram will help physicians to determine which patients will receive the most benefit from lymph node dissection.

Keywords: Non-small cell lung cancer (NSCLC); Surveillance, Epidemiology, and End Results (SEER); lymph node metastasis; nomogram.