Explainable Machine Learning Model to Prediction EGFR Mutation in Lung Cancer

Ruiyuan Yang; Xingyu Xiong; Haoyu Wang; Weimin Li

doi:10.3389/fonc.2022.924144

Explainable Machine Learning Model to Prediction EGFR Mutation in Lung Cancer

Front Oncol. 2022 Jun 23:12:924144. doi: 10.3389/fonc.2022.924144. eCollection 2022.

Authors

Ruiyuan Yang¹, Xingyu Xiong¹, Haoyu Wang¹, Weimin Li^{1

2

3

4}

Affiliations

¹ Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
² Institute of Respiratory Health Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
³ Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
⁴ The Research Units of West China, Chinses Academy of Medical Sciences, West China Hospital, Chengdu, China.

Abstract

Objectives: The aim of this study is to determine whether the clinical features including blood markers can establish an explainable machine learning model to predict epidermal growth factor receptor (EGFR) mutation in lung cancer.

Methods: We retrospectively analyzed 7,413 patients with lung adenocarcinoma (LA) diagnosed by gene sequencing in West China Hospital of the Sichuan University from April 2015 to June 2019. The machine learning algorithms (MLAs) included logistic regression (LR), random forest (RF), LightGBM, support vector machine (SVM), multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and decision tree (DT). Demographic characteristics, personal history, and blood markers were taken into. The area under the receiver operating characteristic curve (AUC) and SHapley Additive exPlanation (SHAP) value were used to explain the prediction models.

Results: Of the 7,413 patients with LA (47.6%), 3,527 were identified with EGFR mutation; RF achieved greatest performance in predicting EGFR mutation AUC [0.771, 95% confidence interval (CI): 0.770, 0.772], which was like XGBoost with AUC (0.740, 95% CI: 0.739, 0.741). The five most influential features were smoking consumption, sex, cholesterol, age, and albumin globulin ratio. The SHAP summary and dependence plot have been used to explain the affection of the 12 features to this model and how a single feature influences the output, respectively.

Conclusion: We established EGFR mutation prediction models by MLAs and revealed that the RF was preferred, AUC (0.771, 95% CI: 0.770, 0.772), which was better than the traditional models. Therefore, the artificial intelligence-based MLA predicting model may become a practical tool to guide in diagnosis and therapy of LA.

Keywords: EGFR mutation; SHAP value; lung cancer; machine learning; prediction.