Relationship between epidermal growth factor receptor mutations and CT features in patients with lung adenocarcinoma

A predictive model of computed tomography and clinical features of EGFR gene mutation in lung adenocarcinoma

Purpose: This study aims to develop a predictive model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma by integrating computed tomography (CT) imaging features with clinical characteristics. Methods: A retrospective analysis was conducted using electronic medical records from 194 patients diagnosed with lung adenocarcinoma between January 2016 and December 2020, with approval from the institutional review board. Features were selected using LASSO regression, and predictive models were built using logistic regression, support vector machine, and random forest methods. Individual models were created for clinical features, CT imaging features, and a combined model to predict EGFR mutations. Results: The training set revealed that alcohol consumption, intrapulmonary metastasis, and pleural effusion were statistically significant in distinguishing between wild-type and mutation groups (p < 0.05). In the testing set, hilar and mediastinal lymphadenopathy showed statistical significance (p < 0.05). The combined model outperformed the individual clinical and CT imaging feature models. In the testing set, the logistic regression model achieved the highest AUC of 0.827, with sensitivity, specificity, and accuracy of 0.714, 0.712, and 0.712, respectively. Nomogram analysis identified lobulation as an important feature, with a predicted probability of up to 0.9. The decision curve analysis showed that the CT imaging feature model provided a higher net benefit compared to both the clinical feature model and the combined model. Conclusion: In summary, while the combined model outperformed the individual feature models in the testing set, the CT imaging feature model demonstrated the greatest clinical net benefit. Lobulation was identified as an important predictor of EGFR mutations in lung adenocarcinoma.

Development of a Nomogram Based on 3D CT Radiomics Signature to Predict the Mutation Status of EGFR Molecular Subtypes in Lung Adenocarcinoma: A Multicenter Study

Background

This study aimed to noninvasively predict the mutation status of epidermal growth factor receptor (EGFR) molecular subtype in lung adenocarcinoma based on CT radiomics features.

Methods

In total, 728 patients with lung adenocarcinoma were included, and divided into three groups according to EGFR mutation subtypes. 1727 radiomics features were extracted from the three-dimensional images of each patient. Wilcoxon test, least absolute shrinkage and selection operator regression, and multiple logistic regression were used for feature selection. ROC curve was used to evaluate the predictive performance of the model. Nomogram was constructed by combining radiomics features and clinical risk factors. Calibration curve was used to evaluate the goodness of fit of the model. Decision curve analysis was used to evaluate the clinical applicability of the model.

Results

There were three, two, and one clinical factor and fourteen, thirteen, and four radiomics features, respectively, which were significantly related to each EGFR molecular subtype. Compared with the clinical and radiomics models, the combined model had the highest predictive performance in predicting EGFR molecular subtypes [Del-19 mutation vs. wild-type, AUC=0.838 (95% CI, 0.799-0.877); L858R mutation vs. wild-type, AUC=0.855 (95% CI, 0.817-0.894); and Del-19 mutation vs. L858R mutation, AUC=0.906 (95% CI, 0.869-0.943), respectively], and it has a stable performance in the validation set [AUC was 0.813 (95% CI, 0.740-0.886), 0.852 (95% CI, 0.790-0.913), and 0.875 (95% CI, 0.781-0.929), respectively].

Conclusion

Our combined model showed good performance in predicting EGFR molecular subtypes in patients with lung adenocarcinoma. This model can be applied to patients with lung adenocarcinoma.