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

Background And Purpose: 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.

Factors associated with the local control of brain metastases: a systematic search and machine learning application.

Background: Enhancing Local Control (LC) of brain metastases is pivotal for improving overall survival, which makes the prediction of local treatment failure a crucial aspect of treatment planning. Understanding the factors that influence LC of brain metastases is imperative for optimizing treatment strategies and subsequently extending overall survival. Machine learning algorithms may help to identify factors that predict outcomes.

The long-term course and relationship with survival of multidimensional fatigue in patients with brain metastases after Gamma Knife radiosurgery.

Purpose: The aims of this study were to evaluate long-term multidimensional fatigue in patients with brain metastases (BM) up to 21 months after Gamma Knife radiosurgery (GKRS) and (change in) fatigue as predictor of survival.

Methods: Patients with 1 to 10 BM, expected survival > 3 months, and Karnofsky Performance Status ≥ 70, and Dutch non-cancer controls were included. Fatigue was measured with the Multidimensional Fatigue Inventory.

Quantifying the Sensitivity of Target Dose on Intrafraction Displacement in Intracranial Stereotactic Radiosurgery.

Purpose: Mask-immobilized stereotactic radiosurgery (SRS) using a gating window is an emerging technology. However, the amount of intracranial tumor motion that can be tolerated during treatment while satisfying clinical dosimetric goals is unknown. The purpose of this study was to quantify the sensitivity of target dose to tumor motion.

WHO grade I meningiomas that show regrowth after gamma knife radiosurgery often show 1p36 loss.

WHO grade I meningiomas occasionally show regrowth after radiosurgical treatment, which cannot be predicted by clinical features. There is increasing evidence that certain biomarkers are associated with regrowth of meningiomas. The aim of this retrospective study was to asses if these biomarkers could be of value to predict regrowth of WHO grade I meningiomas after additive radiosurgery.