Machine Learning Models for Predicting Response to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Non-Small Cell Lung Cancer Brain Metastases: A Systematic Review and Meta-Analysis Publisher Pubmed



Hajikarimloo B ; Mohammadzadeh I ; Hashemi R ; Tos SM ; Bahrami E ; Najari D ; Ebrahimi A ; Hasanzade A ; Habibi MA
Source: Clinical and Translational Oncology Published:2026

Abstract

Background: Predicting clinical and radiological outcomes of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in patients with non-small cell lung cancer (NSCLC) and brain metastases (BMs) is crucial for effective patient management. Machine learning (ML)-based models have increasingly been utilized to predict EGFR-TKI response in patients with lung cancer brain metastasis (LCBM). In this study, we aimed to evaluate the predictive performance of ML-based models for EGFR-TKI response prediction. Methods: A comprehensive literature search was conducted using PubMed, Embase, Scopus, and Web of Science from database inception to April 25, 2025. Studies that developed ML-based models to predict EGFR-TKI response were included. Results: Eight studies involving 1322 LCBM patients were included. The included studies used logistic regression (LR), LR with least absolute shrinkage and selection operator (LASSO), decision tree (DT), and a Cox-based deep learning model (DL-Cox). The meta-analysis revealed a pooled area under the curve (AUC) of 0.84 (95% CI 0.78–0.91) and accuracy (ACC) of 0.75 (95% CI 0.62–0.88) with a sensitivity (SEN) of 0.82 (95% CI 0.77–0.87) and a specificity (SPE) of 0.73 (95% CI 0.66–0.80) for prediction of EGFR-TKI response. The meta-analysis of diagnostic odds ratios (DOR) exhibited a pooled DOR of 12.41 (95% CI 7.32–21.04). Conclusions: ML-based models show promising ability to predict EGFR-TKI response in LCBM, supporting their potential to guide treatment selection. However, their use in clinical practice remains limited by small retrospective datasets and lack of external validation. © The Author(s), under exclusive licence to Federacion de Sociedades Espanolas de Oncologia (FESEO) 2025.