The future of mindfulness training is digital, and the future is now.

Digital mindfulness-based interventions (d-MBIs) present a promising path for the scalable dissemination of mindfulness instruction in the 21st century. Smartphone applications and web-based platforms can offer potential advantages over traditional face-to-face formats through enhanced accessibility, standardization, personalization, and efficacy of mindfulness training. A growing body of research has documented that a digital approach to teaching mindfulness can improve measures of attention, stress, depression, and anxiety. However, effective digital mindfulness instruction must overcome a variety of challenges, including the possibility of low engagement, shallow learning, and unaddressed obstacles or frustrations. Fortunately, best practices from multiple fields of research provide strategies to overcome these challenges.

Recurrent glioblastoma treated with bevacizumab: contrast-enhanced T1-weighted subtraction maps improve tumor delineation and aid prediction of survival in a multicenter clinical trial.

PURPOSE: To compare the capability to aid prediction of clinical outcome measures, including progression-free survival (PFS) and overall survival (OS), between volumetric estimates from contrast material-enhanced (CE) T1-weighted subtraction maps and traditional segmentation in a randomized multicenter clinical trial of recurrent glioblastoma (GBM) patients treated with bevacizumab. MATERIALS AND METHODS: All patients participating in this study signed institutional review board-approved informed consent at their respective institutions prior to enrolling in the multicenter clinical trial. One-hundred sixty patients with recurrent GBM enrolled as part of a HIPAA-compliant, multicenter clinical trial (AVF3708 g, BRAIN trial). Contrast-enhancing tumor volumes and change in volumes as a response to therapy were quantified by using either conventional segmentation or CE T1-weighted subtraction maps created by voxel-by-voxel subtraction of intensity-normalized nonenhanced T1-weighted images from CE T1-weighted images. These volumes were then tested as predictors of PFS and OS by using log-rank univariate analysis, the multivariate Cox proportional hazards regression model, and receiver operating characteristic analysis. RESULTS: Use of CE T1-weighted subtraction maps qualitatively improved visualization and improved quantification of tumor volume after bevacizumab treatment. Significant trends between the volume of tumor and change in tumor volume after therapy on CE T1-weighted subtraction maps were found for both PFS and OS (pretreatment volume < 15 cm(3), P < .003; posttreatment volume < 7.5 cm(3), P < .05; percentage change in volume > 25%, P = .004 for PFS and P = .053 for OS). CE T1-weighted subtraction maps were significantly better at aiding prediction of 6-month PFS and 12-month OS compared with conventional segmentation by using receiver operating characteristic analysis (P < .05). CONCLUSION: Use of CE T1-weighted subtraction maps improved visualization and aided better prediction of patient survival in recurrent GBM treated with bevacizumab compared with conventional segmentation of CE T1-weighted images. Clinical trial registration no. NCT00345163. Online supplemental material is available for this article.