Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.

Background: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the prediction of overall survival (OS) in recurrent high-grade glioma(HGG) patients undergoing immunotherapy, using deep learning (DL) classification networks along with radiomic signatures derived from manual and convolutional neural networks (CNN) automated segmentation.

Benchmarking the Efficacy of Salvage Systemic Therapies for Recurrent Meningioma: A RANO Group Systematic Review and Meta-analysis to Guide Clinical Trial Design.

Background: Despite advances in our understanding of the molecular underpinnings of meningioma progression and innovations in systemic and local treatments, recurrent meningiomas remain a substantial therapeutic challenge. The objective of this systematic review and meta-analysis is to provide a historical baseline, contemporary analysis, and propose a "rate of probable interest" to inform future clinical trial design and development on behalf of the RANO meningioma group.

Methods: PubMed, ClinicalTrials.

Impact of Higher Cell-Free DNA Yields on Liquid Biopsy Testing in Glioblastoma Patients.

Background: Minimally invasive molecular profiling using cell-free DNA (cfDNA) is increasingly important to the management of cancer patients; however, low sensitivity remains a major limitation, particularly for brain tumor patients. Transiently attenuating cfDNA clearance from the body-thereby, allowing more cfDNA to be sampled-has been proposed to improve the performance of liquid biopsy diagnostics. However, there is a paucity of clinical data on the effect of higher cfDNA recovery.