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). The purpose of this study is to evaluate whether certain characteristics of the rectal spacer, as determined on postimplant imaging, were associated with change in bowel QOL at 3-months. METHODS AND MATERIALS: This is a secondary analysis of the 136 patients who received the HA spacer on the randomized trial. Postimplant spacer characteristics (ie, prostate-rectum spacing at superior/midgland/inferior/apex planes, symmetry, prostate volume, spacer volume) were systematically analyzed from structure sets using custom software code. Characteristics demonstrating significant associations with rectal V30 on multivariate linear regression were identified. Linear regression models were used to analyze the associations of such characteristics with change (baseline to 3 months) in both bowel and urinary QOL. RESULTS: Apical spacing (mean 9.4 (standard deviation 4.0)) was significantly smaller than spacing measurements at more superior planes. 95.6% of patients had a symmetrical implant. Apical spacing (P < .001) and prostate volume (P = .01) were significantly associated with rectal V30 on multivariate analysis. However, only apical spacing (0.38/mm; P = .01) was associated with change in bowel QOL, even with adjustment of baseline bowel score (-0.33; P < .01). Percentages of patients with bowel MCID were 14.8% for >= 10 mm versus 36.6% for <10 mm apical spacing (P = .01). Apical spacing was not associated with change in urinary QOL (-0.09; P = .72), when adjusted for baseline urinary QOL (-0.52; P < .01). CONCLUSION: Greater apical spacing was associated with improved rectal dosimetry and smaller decline in bowel QOL at 3-months. Further prospective data are needed to fully understand the ramifications of increased apical spacing.

Technical Note: The use of DirectDensityTM 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. The HU values of various inserts were measured. The RANDO® man phantom was also scanned. Bone was contoured and then histograms of the bone HU values were analyzed for Filtered-Backprojection (FBP) and DD reconstructions of the 80 and 140 kV scans, as well as several virtual, monoenergetic reconstructions generated from FBP and DD reconstructions. "Standard" dose distributions were calculated on several reconstructions of both phantoms for comparison. RESULTS: The DD kernel overcorrected the high-Z material inserts relative to bone, giving an excessively low relative electron density (RED). A unique artifact was observed in the high density inserts of the CatPhan in the monoenergetic scans when utilizing a DD kernel, due to the overcorrection in the DD scan of the material, especially at lower kV. CONCLUSIONS: While DD and DECT perform as expected when used independently, errors from their combined use were demonstrated. Dose errors from misuse of the DD kernel with DECT post-processing were as large as 2.5%. The DECT post-processing was without value because the HU differences between low and high energy were removed by the DD kernel. When using DD and DECT, we recommend the use of a DD reconstruction of the high energy scan for the dose calculation, and use of a FBP filter for the low and high energy scans for the DECT post-processing.

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). METHODS: One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. RESULTS: Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. CONCLUSIONS: By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients.

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. By introducing a specifically designed exponential weighting function to the residual term in residual complexity, the proposed similarity measure performed well due to automatically weighting the residual image between the reference image and the warped floating image. We utilized local variance of the reference image to model the exponential weighting function. The proposed technique was applied to brain magnetic resonance images, dynamic contrast enhanced magnetic resonance images (DCE-MRI) of breasts and contrast enhanced 3D CT liver images. The experimental results clearly indicated that the proposed approach has achieved more accurate and robust performance than mutual information, residual complexity and Jensen-Tsallis.