Journal Article

Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study

Li, Yuan, Tan, Xueyun, Wang, Sufei, Ma, Yanling, Li, Yan, Jin, Yang, Meng, Rui, Xu, Juanjuan, Duan, Yanran, Yang, Guanghai, Xia, Hui

2022

Overview

Background Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology and serum tumor markers (STMs). Methods In this single-center study, demographic features, pathology, EGFR mutation status, ALK rearrangement, and levels of STMs were collected from Wuhan Union Hospital. One retrospective set (N = 1089) was used to train diagnostic performance using one deep learning model and five machine learning models, as well as the stacked ensemble model for predicting EGFR mutations, uncommon EGFR mutations, and ALK rearrangement status. A consecutive testing cohort (n = 1464) was used to validate the predictive models. Results The final AI model using the stacked ensemble yielded optimal diagnostic performance with areas under the curve (AUC) of 0.897 and 0.883 for predicting EGFR mutation status and 0.995 and 0.921 for predicting ALK rearrangement in the training and testing cohorts, respectively. Furthermore, an overall accuracy of 0.93 and 0.83 in the training and testing cohorts, respectively, were achieved in distinguishing common and uncommon EGFR mutations, which were key evidence in guiding TKI selection. Conclusions In this study, driverless AI based on robust variables could help clinicians identify EGFR mutations and ALK rearrangement status and provide vital guidance in TKI selection for targeted therapy in NSCLC patients.

• Publisher: BioMed Central,BioMed Central Ltd,Nature Publishing Group,BMC
• Subject: Anaplastic lymphoma kinase / Anaplastic Lymphoma Kinase - genetics / Antigens / Artificial Intelligence / Biomarkers / Biomarkers, Tumor / Biopsy / Cancer therapies / Carcinoma, Non-Small-Cell Lung - drug therapy / Carcinoma, Non-Small-Cell Lung - genetics / Carcinoma, Non-Small-Cell Lung - pathology / Chromosome Aberrations / Cohort analysis / Cohort Studies / Complications and side effects / Deep learning / Demographics / Demography / Diagnostic systems / Epidermal growth factor / Epidermal growth factor receptor / Epidermal growth factor receptors / ErbB Receptors - genetics / Gene mutations / Generalized linear models / Genetic aspects / Growth factors / Health aspects / Humans / Kinases / Lung cancer / Lung cancer, Non-small cell / Lung Neoplasms - drug therapy / Lung Neoplasms - genetics / Lung Neoplasms - pathology / Lymphoma / Machine learning / Medicine / Medicine & Public Health / Metastasis / Mutation / Mutation - genetics / Non-small cell lung cancer / Non-small cell lung carcinoma / Pathology / Patient outcomes / Patients / Pneumology/Respiratory System / Prediction models / Prostate / Protein-tyrosine kinase / Retrospective Studies / Serum tumor markers / Small cell lung carcinoma / Training / Tumor markers / Tumors / Tyrosine