Development of a prognostic prediction model for non-smoking lung adenocarcinoma based on pathological information and laboratory hematologic indicators: a multicenter study
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View abstract on PubMed
Summary
This summary is machine-generated.A new prognostic model accurately predicts survival in non-smoking lung adenocarcinoma patients using tumor stage, size, and simple blood markers. This model aids personalized treatment decisions for lung cancer patients.
Area Of Science
- Oncology
- Medical Diagnostics
- Biostatistics
Background
- Lung adenocarcinoma is a leading cause of cancer death.
- Accurate prognostic models are crucial for non-smoking patients.
- Existing models may not fully integrate hematologic indicators.
Purpose Of The Study
- To develop a practical prognostic model for non-smoking lung adenocarcinoma patients.
- To combine pathological information with hematologic indicators for survival prediction.
- To establish a nomogram for evaluating prognostic impact.
Main Methods
- Retrospective analysis of 1,172 non-smoking lung adenocarcinoma patients.
- Cox univariate and multivariate analyses to identify significant variables.
- Construction of a Cox proportional hazards model and a nomogram.
Main Results
- Multivariate analysis identified tumor TNM stage, size, WBC count, neutrophil%, lymphocyte%, and hemoglobin as significant predictors.
- The model demonstrated strong predictive performance with C-indices of 0.811 (training), 0.786 (test), and 0.810 (validation).
- AUC values for 3- and 5-year overall survival were consistently high across datasets, indicating effective outcome discrimination.
Conclusions
- A prognostic model integrating tumor characteristics and hematologic markers effectively predicts survival in non-smoking lung adenocarcinoma.
- The model's indicators are readily available and require no conversion, simplifying clinical application.
- This provides a valuable tool for personalized diagnosis and treatment strategies in clinical practice.
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