Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases.

ABSTRACT: The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for radiotherapy. By using various machine-learning algorithms, including random forest, support vector machine, gradient boosting machine, XGBoost, decision tree, artificial neural network, k-nearest neighbors, LightGBM, and CatBoost algorithms, a stacking ensemble model was developed to classify gross tumor volume (GTV), brainstem, and normal brain tissue based on radiomic features. Multiple evaluation metrics, including the specificity, sensitivity, negative predictive value, positive predictive value, accuracy, Matthews correlation coefficient,

SUBMITTER: Zhang HW 

PROVIDER: S-EPMC11577090 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature