ArticleTranslational lung cancer research2021
A nomogram prognostic model for large cell lung cancer: analysis from the Surveillance, Epidemiology and End Results Database.
Article in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 16 citations in OpenAlex.
- Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database.Technology in cancer research & treatmentTrial
- Development of a nomogram for overall survival prediction in primary upper lobe lung cancer patients: A SEER population-based analysis.PloS one · 2025Article
- Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.PloS one · 2024Article
- Whole-exome and targeted gene sequencing of large-cell lung carcinoma reveals recurrent mutations in the PI3K pathway.British journal of cancer · 2023Article
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- Effect of Chemotherapy in Stage II-IV Large-Cell Lung Carcinoma and Construction of Its Predictive Nomograms: A SEER Analysis.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2023Article
- Construction and validation of a nomogram model to predict the overall survival rate of esophageal cancer patients receiving neoadjuvant chemotherapy: A population-based study.Frontiers in surgery · 2022Article
- A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?Annals of translational medicine · 2021Review
- Nomograms-based prediction of overall and cancer-specific survivals for patients diagnosed with major salivary gland carcinoma.Annals of translational medicine · 2021Article
- Second Primary Malignancies in Patients with Pancreatic Neuroendocrine Neoplasms: A Population-Based Study on Occurrence, Risk Factors, and Prognosis.Journal of oncology · 2021Article
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5 authors at 2 institutions in 1 country.
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Abstract
backgroundCurrently, there is no reliable method for predicting the prognosis of patients with large cell lung cancer (LCLC). The aim of this study was to develop and validate a nomogram model for accurately predicting the prognosis of patients with LCLC.
methodsLCLC patients, diagnosed from 2007 to 2009, were identified from the Surveillance, Epidemiology and End Results (SEER) database and used as the training dataset. Significant clinicopathologic variables (P<0.05) in a multivariate Cox regression were selected to build the nomogram. The performance of the nomogram model was evaluated by the concordance index (C-index), the area under the curve (AUC), and internal calibration. LCLC patients diagnosed from 2010 to 2016 in the SEER database were selected as a testing dataset for external validation. The nomogram model was also compared with the currently used American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (8
resultsEight variables-age, sex, race, marital status, T stage, N stage, M stage, and treatment strategy-were statistically significant in the multivariate Cox model and were selected to develop the nomogram model. This model exhibited excellent predictive performance. The C-index and AUC value were 0.761 [95% confidence interval (CI), 0.754 to 0.768] and 0.886 for the training dataset and 0.773 (95% CI, 0.765 to 0.781) and 0.876 for the testing dataset, respectively. This model also predicted three-year and five-year lung cancer-specific survival (LCSS) in both datasets with good fidelity. This nomogram model performs significantly better than the 8th edition AJCC TNM staging system, with a higher C-index (P<0.001) and better net benefits in predicting LCSS in LCLC patients.
conclusionsWe developed and validated a prognostic nomogram model for predicting 3- and 5-year LCSS in LCLC patients with good discrimination and calibration abilities. The nomogram may be useful in assisting clinicians to make individualized decisions for appropriate treatment in LCLC.
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