Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

backgroundEpidermal growth factor receptor (EGFR) mutations are present in 10-60% of all non-small cell lung cancer (NSCLC) patients and are associated with dismal prognosis. Lung cancer brain metastases (LCBM) are a common complication of lung cancer. Predictions of EGFR can help physicians in decision-making and, through optimizing treatment strategies, can result in more favorable outcomes. This systematic review and meta-analysis evaluated the predictive performance of machine learning (ML)-based models in EGFR status in NSCLC patients with brain metastasis.

methodsOn December 20, 2024, the four electronic databases, Pubmed, Embase, Scopus, and Web of Science, were systematically searched. Studies that evaluated EGFR status in patients with brain metastasis from NSCLC were included.

resultsTwenty studies with 3517 patients with 6205 NSCLC brain metastatic lesions were included. The majority of the best-performance models were ML-based (70%, 7/10), and deep learning (DL)-based models comprised 30% (6/20) of models. The area under the curve (AUC) and accuracy (ACC) of the best-performance models ranged from 0.765 to 1 and 0.69 to 0.93, respectively. The meta-analysis of the best-performance model revealed a pooled AUC of 0.91 (95%CI: 0.88-0.93) and ACC of 0.82 (95%CI: 0.79-0.86) along with a pooled sensitivity of 0.87 (95%CI: 0.83-0.9), specificity of 0.86 (95%CI: 0.79-0.9), and diagnostic odds ratio (DOR) of 35.2 (95%CI: 21.2-58.4). The subgroup analysis did not show significant differences between ML and DL models.

conclusionML-based models demonstrated promising predictive outcomes in predicting EGFR status. Applying ML-based models in daily clinical practice can optimize treatment strategies and enhance clinical and radiological outcomes.