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Updated: May 17, 2025
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
Development of a prognostic prediction model for non-smoking lung adenocarcinoma based on pathological information
Run Xiang1,2, Peihong Hu2, Xiaoxiong Xiao3,4
1State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
Objective:
To develop a simple and practical model to predict the prognostic survival of non-smoking patients with lung adenocarcinoma by combining general pathological information with laboratory hematologic indicators.
Methods:
Cox univariate and multivariate analyses were used to identify the variable indicators. A Cox proportional hazards model was constructed based on the selected variables to compare survival outcomes between the high-and low-risk groups of non-smoking patients with lung adenocarcinoma and to validate the model's performance. Subsequently, a nomogram model was established to systematically evaluate the impact of selected variables on prognosis.
Results:
Data of non-smoking patients with lung adenocarcinoma from four hospitals were retrospectively collected. We enrolled 1,172 patients, this includes 372 external validation data. Multivariate analysis identified six significant variables (P < 0.05): tumor TNM stage, tumor size, white blood cell count, neutrophil percentage, lymphocyte percentage, and hemoglobin level. We combined these six variables to build a model. The C-index of the training set is 0.811 (0.780-0.842), this value is 0.786 (0.737-0.835) in,test set and 0.810 (0.772-0.847) in validation set. The area under the curve (AUC) results of the predicted 3-years overall survival (OS) of the three data sets were 0.850, 0.819, and 0.860, respectively. These values for 5-years were 0.811, 0.771, and 0.849. Stratified analysis based on tumor staging showed that the model effectively distinguished outcomes (P < 0.0001). High-risk groups demonstrated significantly poorer prognosis compared to low-risk groups (P < 0.001).
Conclusion:
The prognostic model based on tumor TNM stage, tumor size, white blood cell count, neutrophil percentage, lymphocyte percentage, and hemoglobin levels effectively predicted the prognosis of non-smoking patients with lung adenocarcinoma. Compared with the more studied blood markers at present, the indicators of our model do not need conversion, Our model provides a useful reference for personalized diagnosis and treatment in clinical practice.
