A population-based prediction model for lymph node metastasis in patients aged ≥70 years with non-small cell lung cancer.
- 1Department of Respiratory Diseases, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Abstract
Accurate assessment of lymph node metastasis (LNM) is important for staging and treatment planning in older patients with non-small cell lung cancer (NSCLC), but invasive nodal evaluation may be limited by comorbidities and reduced physiologic reserve. We aimed to characterize the burden of LNM and develop a prediction model for patients aged ≥70 years with NSCLC. Patients aged ≥70 years with histologically confirmed NSCLC were identified from the Surveillance, Epidemiology, and End Results database and randomly split into training (70%) and internal validation (30%) cohorts. Independent predictors of LNM were identified using multivariable logistic regression, and a nomogram was constructed. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis. A total of 59,128 patients were included. In 2022, LNM was identified in 48.2% of patients. Age, sex, race, histologic subtype, tumor laterality, tumor location, tumor grade, T stage, M stage, tumor size, and bone, lung, and liver metastases were independent predictors of LNM. The model showed acceptable discrimination, with AUCs of 0.78 and 0.79 in the training and internal validation cohorts, respectively. LNM was associated with higher lung cancer-specific mortality and lower non-lung cancer mortality. We developed a practical, noninvasive prediction model for LNM in older patients with NSCLC, which may support individualized risk stratification and clinical management.
Related
- CT-based multi-station lymph-node radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a proof-of-concept comparison with primary-tumor radiomics
- Quantification of fat content in the prediction of lymph node metastasis in patients with head and neck squamous cell carcinoma: a study based on dixon-MRI
- Habitat-based ultrasound radiomics for preoperative prediction of axillary lymph node metastasis in breast cancer: a two-center study
- MR-based radiomics of mesorectal fat for improved prediction of perirectal lymph node metastasis and extramural venous invasion in locally advanced rectal cancer
- Investigating the potential association between the continuous-time random-walk MR diffusion model and lymph node metastasis and vascular invasion in patients with rectal cancer: A preliminary exploratory study
BibTeX
@article{xing2026populationbased,
title = {{A population-based prediction model for lymph node metastasis in patients aged ≥70 years with non-small cell lung cancer.}},
author = {Hongquan Xing and Weichang Yang and Sunying Li and Yang Huang and Xiaoqun Ye and Guofeng Zhu},
journal = {Translational cancer research},
year = {2026},
month = sep,
doi = {10.21037/tcr-2026-1926},
url = {https://europepmc.org/article/MED/42997900},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A population-based prediction model for lymph node metastasis in patients aged ≥70 years with non-small cell lung cancer.
AU - Hongquan Xing
AU - Weichang Yang
AU - Sunying Li
AU - Yang Huang
AU - Xiaoqun Ye
AU - Guofeng Zhu
PY - 2026
DA - 2026/09/28
JO - Translational cancer research
DO - 10.21037/tcr-2026-1926
UR - https://europepmc.org/article/MED/42997900
AB - Accurate assessment of lymph node metastasis (LNM) is important for staging and treatment planning in older patients with non-small cell lung cancer (NSCLC), but invasive nodal evaluation may be limited by comorbidities and reduced physiologic reserve. We aimed to characterize the burden of LNM and develop a prediction model for patients aged ≥70 years with NSCLC. Patients aged ≥70 years with histologically confirmed NSCLC were identified from the Surveillance, Epidemiology, and End Results database and randomly split into training (70%) and internal validation (30%) cohorts. Independent predictors of LNM were identified using multivariable logistic regression, and a nomogram was constructed. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis. A total of 59,128 patients were included. In 2022, LNM was identified in 48.2% of patients. Age, sex, race, histologic subtype, tumor laterality, tumor location, tumor grade, T stage, M stage, tumor size, and bone, lung, and liver metastases were independent predictors of LNM. The model showed acceptable discrimination, with AUCs of 0.78 and 0.79 in the training and internal validation cohorts, respectively. LNM was associated with higher lung cancer-specific mortality and lower non-lung cancer mortality. We developed a practical, noninvasive prediction model for LNM in older patients with NSCLC, which may support individualized risk stratification and clinical management.
SN - 2218-676X
SN - 2219-6803
ER -