Prediction of EGFR Mutation Status in Non–Small Cell Lung Cancer Based on Ensemble Learning
Objectives: We aimed to identify whether ensemble learning can improve the performance of the epidermal growth factor receptor (EGFR) mutation status predicting model.Methods: We retrospectively collected 168 patients with non–small cell lung cancer (NSCLC), who underwent both computed tomography (C...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-06-01
|
| Series: | Frontiers in Pharmacology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2022.897597/full |
| _version_ | 1811237686665543680 |
|---|---|
| author | Youdan Feng Fan Song Peng Zhang Guangda Fan Tianyi Zhang Xiangyu Zhao Chenbin Ma Yangyang Sun Xiao Song Huangsheng Pu Fei Liu Guanglei Zhang |
| author_facet | Youdan Feng Fan Song Peng Zhang Guangda Fan Tianyi Zhang Xiangyu Zhao Chenbin Ma Yangyang Sun Xiao Song Huangsheng Pu Fei Liu Guanglei Zhang |
| author_sort | Youdan Feng |
| collection | DOAJ |
| description | Objectives: We aimed to identify whether ensemble learning can improve the performance of the epidermal growth factor receptor (EGFR) mutation status predicting model.Methods: We retrospectively collected 168 patients with non–small cell lung cancer (NSCLC), who underwent both computed tomography (CT) examination and EGFR test. Using the radiomics features extracted from the CT images, an ensemble model was established with four individual classifiers: logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). The synthetic minority oversampling technique (SMOTE) was also used to decrease the influence of data imbalance. The performances of the predicting model were evaluated using the area under the curve (AUC).Results: Based on the 26 radiomics features after feature selection, the SVM performed best (AUCs of 0.8634 and 0.7885 on the training and test sets, respectively) among four individual classifiers. The ensemble model of RF, XGBoost, and LR achieved the best performance (AUCs of 0.8465 and 0.8654 on the training and test sets, respectively).Conclusion: Ensemble learning can improve the model performance in predicting the EGFR mutation status of patients with NSCLC, showing potential value in clinical practice. |
| first_indexed | 2024-04-12T12:27:43Z |
| format | Article |
| id | doaj.art-5f74726c605c423b9e8f807b8594e19f |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-12T12:27:43Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-5f74726c605c423b9e8f807b8594e19f2022-12-22T03:33:07ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122022-06-011310.3389/fphar.2022.897597897597Prediction of EGFR Mutation Status in Non–Small Cell Lung Cancer Based on Ensemble LearningYoudan Feng0Fan Song1Peng Zhang2Guangda Fan3Tianyi Zhang4Xiangyu Zhao5Chenbin Ma6Yangyang Sun7Xiao Song8Huangsheng Pu9Fei Liu10Guanglei Zhang11Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, ChinaBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, ChinaBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, ChinaBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, ChinaBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, ChinaBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, ChinaBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, ChinaBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, ChinaSchool of Medical Imaging, Shanxi Medical University, Taiyuan, ChinaCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, ChinaBeijing Advanced Information and Industrial Technology Research Institute, Beijing Information Science and Technology University, Beijing, ChinaBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, ChinaObjectives: We aimed to identify whether ensemble learning can improve the performance of the epidermal growth factor receptor (EGFR) mutation status predicting model.Methods: We retrospectively collected 168 patients with non–small cell lung cancer (NSCLC), who underwent both computed tomography (CT) examination and EGFR test. Using the radiomics features extracted from the CT images, an ensemble model was established with four individual classifiers: logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). The synthetic minority oversampling technique (SMOTE) was also used to decrease the influence of data imbalance. The performances of the predicting model were evaluated using the area under the curve (AUC).Results: Based on the 26 radiomics features after feature selection, the SVM performed best (AUCs of 0.8634 and 0.7885 on the training and test sets, respectively) among four individual classifiers. The ensemble model of RF, XGBoost, and LR achieved the best performance (AUCs of 0.8465 and 0.8654 on the training and test sets, respectively).Conclusion: Ensemble learning can improve the model performance in predicting the EGFR mutation status of patients with NSCLC, showing potential value in clinical practice.https://www.frontiersin.org/articles/10.3389/fphar.2022.897597/fullnon–small cell lung cancerradiogenomicsEGFRcomputed tomographyensemble learning |
| spellingShingle | Youdan Feng Fan Song Peng Zhang Guangda Fan Tianyi Zhang Xiangyu Zhao Chenbin Ma Yangyang Sun Xiao Song Huangsheng Pu Fei Liu Guanglei Zhang Prediction of EGFR Mutation Status in Non–Small Cell Lung Cancer Based on Ensemble Learning Frontiers in Pharmacology non–small cell lung cancer radiogenomics EGFR computed tomography ensemble learning |
| title | Prediction of EGFR Mutation Status in Non–Small Cell Lung Cancer Based on Ensemble Learning |
| title_full | Prediction of EGFR Mutation Status in Non–Small Cell Lung Cancer Based on Ensemble Learning |
| title_fullStr | Prediction of EGFR Mutation Status in Non–Small Cell Lung Cancer Based on Ensemble Learning |
| title_full_unstemmed | Prediction of EGFR Mutation Status in Non–Small Cell Lung Cancer Based on Ensemble Learning |
| title_short | Prediction of EGFR Mutation Status in Non–Small Cell Lung Cancer Based on Ensemble Learning |
| title_sort | prediction of egfr mutation status in non small cell lung cancer based on ensemble learning |
| topic | non–small cell lung cancer radiogenomics EGFR computed tomography ensemble learning |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2022.897597/full |
| work_keys_str_mv | AT youdanfeng predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT fansong predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT pengzhang predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT guangdafan predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT tianyizhang predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT xiangyuzhao predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT chenbinma predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT yangyangsun predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT xiaosong predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT huangshengpu predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT feiliu predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning AT guangleizhang predictionofegfrmutationstatusinnonsmallcelllungcancerbasedonensemblelearning |