Development of a multimodal fully automated ensemble model to predict EGFR mutation and efficacy of EGFR-TKI in non-small cell lung cancer.

ABSTRACT:

Background

Drugs for systemic therapy to advanced or recurrent non-small cell lung cancer (NSCLC) are determined by the type of driver oncogenes and programmed death-ligand 1 (PD-L1) immunostaining. In this study, we investigated the potential for artificial intelligence to assist in detecting epidermal growth factor receptor (EGFR) mutation, which is one of the most frequent and important driver oncogenes, and predicting the efficacy of EGFR-tyrosine kinase inhibitors (TKIs) in patients with NSCLC.

Methods

We built an ensemble model that combined machine learning models (logistic regression, Bernoulli naive Bayes and Gaussian naive Bayes) for clinical information and tumor characteristics analysis, and a deep convolutional neural network for analyzing computed tomography (C

SUBMITTER: Xu S 

PROVIDER: S-EPMC12775711 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature