ArticleChinese medical journal2017
A Model Predicting Lymph Node Status for Patients with Clinical Stage T1aN0-2M0 Nonsmall Cell Lung Cancer.
Article in Chinese medical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Trial
- Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020Trial
- Risk profiles and a concise prediction model for lymph node metastasis in patients with lung adenocarcinoma.Journal of cardiothoracic surgery · 2023Review
- DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm.Frontiers in oncology · 2021Article
- Preoperative Risk Assessment of Lymph Node Metastasis in cT1 Lung Cancer: A Retrospective Study from Eastern China.Journal of immunology research · 2019Article
- Young age increases risk for lymph node positivity but decreases risk for non-small cell lung cancer death.Cancer management and research · 2018Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLymph node status of patients with early-stage nonsmall cell lung cancer has an influence on the choice of surgery. To assess the lymph node status more correspondingly and accurately, we evaluated the relationship between the preoperative clinical variables and lymph node status and developed one model for predicting lymph node involvement.
methodsWe collected clinical and dissected lymph node information of 474 patients with clinical stage T1aN0-2M0 nonsmall cell lung cancer (NSCLC). Logistic regression analysis of clinical characteristics was used to estimate independent predictors of lymph node metastasis. The prediction model was validated by another group.
resultsEighty-two patients were diagnosed with positive lymph nodes (17.3%), and four independent predictors of lymph node disease were identified: larger consolidation size (odds ratio [OR] = 2.356, 95% confidence interval [CI]: 1.517-3.658, P < 0.001,), central tumor location (OR = 2.810, 95% CI: 1.545-5.109, P = 0.001), abnormal status of tumor marker (OR = 3.190, 95% CI: 1.797-5.661, P < 0.001), and clinical N1-N2 stage (OR = 6.518, 95% CI: 3.242-11.697, P < 0.001). The model showed good calibration (Hosmer-Lemeshow goodness-of-fit, P < 0.766) with an area under the receiver operating characteristics curve (AUC) of 0.842 (95% [CI]: 0.797-0.886). For the validation group, the AUC was 0.810 (95% CI: 0.731-0.889).
conclusionsThe model can assess the lymph node status of patients with clinical stage T1aN0-2M0 NSCLC, enable surgeons perform an individualized prediction preoperatively, and assist the clinical decision-making procedure.
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