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Introduction: In diffuse intrinsic pontine gliomas (DIPG), subtracting pre-contrast from post-contrast T1-weighted images (T1WI) occasionally reveals subtle, 'occult' enhancement. We hypothesized that this represents intravascular enhancement related to angiogenesis and hence that these tumors should have greater blood volume fractions than do non-enhancing tumors. Methods: We retrospectively screened MR images of 66 patients initially diagnosed with DIPG and analyzed pretreatment conventional and dynamic susceptibility contrast (DSC) perfusion MRI studies of 61 patients. To determine the incidence of occult enhancement, cerebral blood volume (CBV) values were compared in areas of occult enhancement (OcE), no enhancement (NE), and normal-appearing deep cerebellar white matter (DCWM). Results: Tumors of 10 patients (16.4 %) had occult enhancement; those of 6 patients (9.8 %) had no enhancement at all. The average CBV in areas of occult enhancement was significantly higher than that in non-enhancing areas of the same tumor ( P = .03), within DCWM in the same patient ( P = .03), and when compared to anatomically paired/similar regions of interest (ROI) in patients with non-enhancing tumors ( P = .005). Conclusion: Areas of OcE correspond to areas of higher CBV in DIPG, which may be an MRI marker for angiogenesis, but larger scale studies may be needed to determine its potential relevance to grading by imaging, treatment stratification, biopsy guidance, and evaluation of response to targeted therapy. [ABSTRACT FROM AUTHOR]

ABSTRACT Aims Smoking cessation trials generally record information on daily smoking behavior, but base analyses on measures of smoking status at the end of treatment (EOT). We present an alternative approach that analyzes the entire sequence of daily smoking status observations. Methods We analyzed daily abstinence data from a smoking cessation trial, using two longitudinal logistic regression methods: a mixed-effects (ME) model and a generalized estimating equations (GEE) model. We compared results to a standard analysis that takes abstinence status at EOT as outcome. We evaluated time-varying covariates (smoking history and time-varying drug effect) in the longitudinal analysis and compared ME and GEE approaches. Results We observed some differences in the estimated treatment effect odds ratios across models, with narrower confidence intervals under the longitudinal models. GEE yields similar results to ME when only baseline factors appear in the model, but gives biased results when one includes time-varying covariates. The longitudinal models indicate that the quit probability declines and the drug effect varies over time. Both the previous day's smoking status and recent smoking history predict quit probability, independently of the drug effect. Conclusion When analysing outcomes of studies from smoking cessation interventions, longitudinal models with multiple outcome data points, rather than just end of treatment, can makes efficient use of the data and incorporate time-varying covariates. The generalized estimating equations approach should be avoided when using time-varying predictors. [ABSTRACT FROM AUTHOR]