Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features

Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learning (DL) features to clinical features increased Local Control (LC) prediction accuracy, their combined potential to predict LC remains unexplored. We examined whether a model using a combination of radiomics, DL and clinical features achieves better accuracy than models using only a subset of these features.