Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, brain metastasis
Motivation: Lung cancer is the most common primary tumor showing brain metastasis (BM). Epidermal growth factor receptor (EGFR) mutations are detected in a significant proportion of lung cancer patients.
Goal(s): However, a subset of patients may show discordance in EGFR mutation status between the primary lung tumor and the corresponding BMs, which may affect decision-making in treatments.
Approach: We used machine learning (ML) based on pretreatment brain MRI and clinical data for prediction of EGFR mutation status in BMs of lung cancer.
Results: Among various ML algorithms, the best predictive performance with accuracy of 89%, precision of 88%, and AUC of 0.97 were obtained.
Impact: Machine learning based on pretreatment clinical data and brain MRI provides the potential to predict the EGFR mutation status in brain metastasis of lung cancer, and may affect decision-making in treatments.
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