Symmetry, separation, and stability: Physical properties for effective dosimetric space with a stabilized hyaluronic acid spacer.

Background: The proximity of the rectum to the prostate in radiation therapy (RT) for prostate cancer presents a significant dosimetric challenge, leading to high rectal doses and resulting in detrimental side effects. Perirectal tissue spacing reduces rectal dose and gastrointestinal toxicities by mechanically separating these organs. A variety of materials have been explored for use as rectal spacers, most recently, a stabilized hyaluronic acid (HA) gel, which can be formed into deliberate a shape, and retains the definition of that shape, while remaining flexible, unlike polyethylene glycol (PEG) hydrogels.

Evaluating the Quality-of-Life Effect of Apical Spacing with Hyaluronic Acid Prior to Hypofractionated Prostate Radiation Therapy: A Secondary Analysis.

Purpose: Recently, a randomized trial demonstrated that a hyaluronic acid (HA) spacer placed before prostate hypofractionated intensity modulated radiation therapy improved rectal dosimetry and reduced acute grade 2+ gastrointestinal toxicity. However, 26.5% of patients receiving the spacer experienced a minimal clinically important decline (MCID) in bowel quality-of-life (QOL).

Technical Note: The use of DirectDensity and dual-energy CT in the radiation oncology clinic.

Purpose: Two new tools available in Radiation Oncology clinics are Dual-energy CT (DECT) and Siemens' DirectDensity™ (DD) reconstruction algorithm, which allows scans of any kV setting to use the same calibration. This study demonstrates why DD scans should not be used in combination with DECT and quantifies the magnitude of potential errors in image quality and dose.

Methods: A CatPhan 504 phantom was scanned with a dual-pass DECT and reconstructed with many different kernels, including several DD kernels.

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

Background: MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB).

Intensity based image registration by minimizing exponential function weighted residual complexity.

In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results.