Differentiation of Cerebral Neoplasms with Vessel Size Imaging (VSI).

PURPOSE: Cerebral neoplasms of various histological origins may show comparable appearances on conventional Magnetic Resonance Imaging (MRI). Vessel size imaging (VSI) is an MRI technique that enables noninvasive assessment of microvasculature by providing quantitative estimates of microvessel size and density. In this study, we evaluated the potential of VSI to differentiate between brain tumor types based on their microvascular morphology. METHODS: Using a clinical 3T MRI scanner, VSI was performed on 25 patients with cerebral neoplasms, 10 with glioblastoma multiforme (GBM), 8 with primary CNS lymphoma (PCNSL) and 7 with cerebral lung cancer metastasis (MLC). Following the postprocessing of VSI maps, mean vessel diameter (vessel size index, vsi) and microvessel density (Q) were compared across tumors, peritumoral areas, and healthy tissues. RESULTS: The MLC tumors have larger and less dense microvasculature compared to PCNSLs in terms of vsi and Q (p = 0.0004 and p < 0.0001, respectively). GBM tumors have higher yet non-significantly different vsi values than PCNSLs (p = 0.065) and non-significant differences in Q. No statistically significant differences in vsi or Q were present between GBMs and MLCs. GBM tumor volume was positively correlated with vsi (r = 0.502, p = 0.0017) and negatively correlated with Q (r = -0.531, p = 0.0007). CONCLUSION: Conventional MRI parameters are helpful in differentiating between PCNSLs, GBMs, and MLCs. Additionally incorporating VSI parameters into the diagnostic protocol could help in further differentiating between PCNSLs and metastases and potentially between PCNSLs and GBMs. Future studies in larger patient cohorts are required to establish diagnostic cut-off values for VSI.

Analysis of covariance under variance heteroscedasticity in general factorial designs.

Adjusting for baseline values and covariates is a recurrent statistical problem in medical science. In particular, variance heteroscedasticity is non-negligible in experimental designs and ignoring it might result in false conclusions. Approximate inference methods are developed to test null hypotheses formulated in terms of adjusted treatment effects and regression parameters in general analysis of covariance designs with arbitrary numbers of factors. Variance homoscedasticity is not assumed. The distributions of the test statistics are approximated using Box-type approximation methods. Extensive simulation studies show that the procedures are particularly suitable when sample sizes are rather small. A real data set illustrates the application of the methods.

Effectiveness of different activated irrigation techniques on debris and smear layer removal from curved root canals: a SEM evaluation.

This study evaluates the effectiveness of different activated irrigation techniques on removal of debris and smear layer from curved root canals. Ninety mandibular molars with a root canal curvature between 20 and 40 degrees were assigned to 4 groups (n = 20): syringe irrigation (SI), passive ultrasonic activation (PUI), sonic activation with EDDY (ED) or EndoActivator (EA) and a control group. Mesiobuccal root canals were prepared to size 40, 0.04 and irrigated with NaOCl (3%) according to the respective technique. Roots were split longitudinally and subjected to scanning electron microscopic analysis. Presence of debris and smear layer was evaluated using 5-grade scoring systems with 200× and 1000× magnification, respectively. Data were analysed with nonparametric analysis for ordinal longitudinal data (α = 5%). Activation of the irrigant significantly improved smear layer removal (P < 0.05). Regarding debris, only activation with EA and ED was significantly more effective than SI (P < 0.05). No activation technique was able to eliminate debris and smear layer completely from curved root canals.