Can machine learning models improve early detection of brain metastases using diffusion weighted imaging-based radiomics?

Background: Metastatic complications are a major cause of cancer-related morbidity, with up to 40% of cancer patients experiencing at least one brain metastasis. Earlier detection may significantly improve patient outcomes and overall survival. We investigated machine learning (ML) models for early detection of brain metastases based on diffusion weighted imaging (DWI) radiomics.

Investigating diffusion patterns of brain metastases pre- and post-stereotactic radiosurgery: a feasibility study.

Metastatic complications are responsible for 90% of cancer-associated mortality. Magnetic resonance imaging (MRI) can be used to observe the brain's microstructure and potentially correlate changes with metastasis occurrence. Diffusion weighted imaging (DWI) is an MRI technique that utilizes the kinetics of water molecules within the body.

Verification of Acuros XB dose algorithm using 3D printed low-density phantoms for clinical photon beams.

The transport-based dose calculation algorithm Acuros XB (AXB) has been shown to accurately account for heterogeneities primarily through comparisons with Monte Carlo simulations. This study aims to provide additional experimental verification of AXB for clinically relevant flattened and unflattened beam energies in low density phantoms of the same material. Polystyrene slabs were created using a bench-top 3D printer.

Characterizing 3D printing in the fabrication of variable density phantoms for quality assurance of radiotherapy.

Purpose: To present characterization, process flow, and applications of 3D fabricated low density phantoms for radiotherapy quality assurance (QA).

Material And Methods: A Rostock 3D printer using polystyrene was employed to print slabs of varying relative electron densities (0.18-0.