Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR
Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning.
10.3779/j.issn.1009-3419.2025.102.05
- Author:
Shulei FU
1
;
Shaodi WEN
1
;
Jiaqiang ZHANG
2
;
Xiaoyue DU
1
;
Ru LI
2
;
Bo SHEN
1
Author Information
1. The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research,
Nanjing 210009, China.
2. Inner Mongolia University, Hohhot 010000, China.
- Publication Type:Journal Article
- Keywords:
Epidermal growth factor receptor;
Lung neplasms;
Machine learning;
Predictive model
- MeSH:
Humans;
Carcinoma, Non-Small-Cell Lung/diagnosis*;
Machine Learning;
Lung Neoplasms/diagnosis*;
Male;
Female;
Mutation;
Middle Aged;
ErbB Receptors/genetics*;
Prognosis;
Aged;
Retrospective Studies;
Adult;
Biomarkers, Tumor/genetics*
- From:
Chinese Journal of Lung Cancer
2025;28(2):105-113
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND:Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic factors in some underdeveloped areas, some patients cannot undergo traditional genetic testing. The aim of this study is to establish a machine learning (ML) model using non-invasive peripheral blood markers to explore the biomarkers closely related to EGFR mutation status in NSCLC and evaluate their potential prognostic value.
METHODS:2642 lung cancer patients who visited Jiangsu Cancer Hospital from November 2016 to May 2023 were retrospectively enrolled and finally 175 NSCLC patients with complete follow-up data were included in the study. The ML model was constructed based on peripheral blood indicators and divided into training set and test set according to the ratio of 8:2. Unsupervised learning algorithms were used for clustering blood features and mutual information method for feature selection, and an ensemble learning algorithm based on Shapley value was designed to calculate the contribution of each feature to the model prediction result. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the model.
RESULTS:Through the feature extraction and contribution analysis of the predictive results of the interpretable ML model based on the Shapley value, the top ten indicators with the highest contribution were: pathological type, phosphorus, eosinophils, monocyte count, activated partial thromboplastin time, potassium, total bilirubin, sodium, eosinophil percentage, and total cholesterol. The area under the curve (AUC) of the model was 0.80. In addition, patients with hyponatremia and squamous cell carcinoma group had a poor prognosis (P<0.05).
CONCLUSIONS:The interpretable model constructed in this study provides a new approach for the prediction of EGFR mutation status in NSCLC patients, which provides a scientific basis for the diagnosis and treatment of patients who cannot undergo genetic testing.
