Computer-extracted MR imaging features are associated with survival in glioblastoma patients

Automatic survival prognosis in glioblastoma (GBM) could result in improved treatment planning for the patient. The purpose of this research is to investigate the association of survival in GBM patients with tumor features in pre-operative magnetic resonance (MR) images assessed using a fully automatic computer algorithm. MR imaging data for 68 patients from two US institutions were used in this study. The images were obtained from the Cancer Imaging Archive. A fully automatic computer vision algorithm was applied to segment the images and extract eight imaging features from the MRI studies. The features included tumor side, proportion of enhancing tumor, proportion of necrosis, T1/FLAIR ratio, major axis length, minor axis length, tumor volume, and thickness of enhancing margin. We constructed a multivariate Cox proportional hazards regression model and used a likelihood ratio test to establish whether the imaging features are prognostic of survival. We also evaluated the individual prognostic value of each feature through multivariate analysis using the multivariate Cox model and univariate analysis using univariate Cox models for each feature. We found that the automatically extracted imaging features were predictive of survival (p = 0.031). Multivariate analysis of individual features showed that two individual features were predictive of survival: proportion of enhancing tumor (p = 0.013), and major axis length (p = 0.026). Univariate analysis indicated the same two features as significant (p = 0.021, and p = 0.017 respectively). We conclude that computer-extracted MR imaging features can be used for survival prognosis in GBM patients.     

Effect of Olive and Sunflower Seed Oil on the Adult Skin Barrier: Implications for Neonatal Skin Care

Natural oils are advocated and used throughout the world as part of neonatal skin care, but there is an absence of evidence to support this practice. The goal of the current study was to ascertain the effect of olive oil and sunflower seed oil on the biophysical properties of the skin. Nineteen adult volunteers with and without a history of atopic dermatitis were recruited into two randomized forearm‐controlled mechanistic studies. The first cohort applied six drops of olive oil to one forearm twice daily for 5 weeks. The second cohort applied six drops of olive oil to one forearm and six drops of sunflower seed oil to the other twice daily for 4 weeks. The effect of the treatments was evaluated by determining stratum corneum integrity and cohesion, intercorneocyte cohesion, moisturization, skin‐surface pH, and erythema. Topical application of olive oil for 4 weeks caused a significant reduction in stratum corneum integrity and induced mild erythema in volunteers with and without a history of atopic dermatitis. Sunflower seed oil preserved stratum corneum integrity, did not cause erythema, and improved hydration in the same volunteers. In contrast to sunflower seed oil, topical treatment with olive oil significantly damages the skin barrier, and therefore has the potential to promote the development of, and exacerbate existing, atopic dermatitis. The use of olive oil for the treatment of dry skin and infant massage should therefore be discouraged. These findings challenge the unfounded belief that all natural oils are beneficial for the skin and highlight the need for further research.