Abstract
Gamma knife radiosurgery (GRKS) is widely used for patients with brain metastases; however, predictions of overall survival (OS) within 3-months post-GKRS remain imprecise. Specifically, more than 10% of non-small cell lung cancer (NSCLC) patients died within 8 weeks of post-GKRS, indicating potential overtreatment. This study aims to predict OS within 3-months post-GKRS using machine learning algorithms, and to identify prognostic features in NSCLC patients. We selected 120 NSCLC patients who underwent GKRS at Chungbuk National University Hospital. They were randomly assigned to training group (n = 80) and testing group (n = 40) with 14 features considered. We used 3 machine learning (ML) algorithms (Decision tree, Random forest, and Boosted tree classifier) to predict OS within 3-months for NSCLC patients. And we extracted important features and permutation features. Data validation was verified by physician and medical physicist. The accuracy of the ML algorithms for predicting OS within 3-months was 77.5% for the decision tree, 72.5% for the random forest, and 70% for the boosted tree classifier. The important features commonly showed age, receiving chemotherapy, and pretreatment each algorithm. Additionally, the permutation features commonly showed tumor volume (>10 cc) and age as critical factors each algorithm. The decision tree algorithm exhibited the highest accuracy. Analysis of the decision tree visualized data revealed that patients aged (>71 years) with tumor volume (>10 cc) were increased risk of mortality within 3-months. The findings suggest that ML algorithms can effectively predict OS within 3-months and identify crucial features in NSCLC patients. For NSCLC patients with poor prognoses, old age, and large tumor volumes, GKRS may not be a desirable treatment.