Triaxial accelerometer-measured physical activity and functional behaviours among people with High Grade Glioma: The BrainWear Study.

BACKGROUND: High-grade gliomas (HGG) account for 60-75% of all adult gliomas. The complexity of treatment, recovery and survivorship creates a need for novel monitoring approaches. Accurate assessment of physical function plays a vital role in clinical evaluation. Digital wearable tools could help us address unmet needs by offering unique advantages such as scale, cost and continuous real-world objective data. We present data from 42 patients enrolled into the BrainWear study. METHODS: An AX3 accelerometer was worn by patients from diagnosis or at recurrence. Age-, sex-matched UK Biobank control groups were chosen for comparison. RESULTS: 80% of data were categorised as high-quality demonstrating acceptability. Remote, passive monitoring identifies moderate activity reduces both during a course of radiotherapy (69 to 16 minutes/day) and at the time of progressive disease assessed by MRI (72 to 52 minutes/day). Mean acceleration (mg) and time spent walking daily (h/day) correlated positively with the global health quality of life and physical functioning scores and inversely with the fatigue score. Healthy controls walked on average 2.91h/day compared to 1.32h/day for the HGG group on weekdays and 0.91h/day on the weekend. The HGG cohort slept for longer on weekends (11.6h/day) than weekdays (11.2h/day) compared to healthy controls (8.9h/day). CONCLUSION: Wrist-worn accelerometers are acceptable and longitudinal studies feasible. HGG patients receiving a course of radiotherapy reduce their moderate activity by 4-fold and are at least half as active as healthy controls at baseline. Remote monitoring can provide a more informed and objective understanding of patient activity levels to help optimise health related quality of life (HRQoL) among a patient cohort with an extremely limited lifespan.

Improving on whole-brain radiotherapy in patients with large brain metastases: A planning study to support the AROMA clinical trial.

PURPOSE: To develop a novel dose-escalated volumetric modulated arc therapy (VMAT) strategy for patients with single or multiple large brain metastases which can deliver a higher dose to individual lesions for better local control (LC), and to compare dosimetry between whole brain radiotherapy (WBRT), hippocampal-sparing whole brain radiotherapy (HS-WBRT) and different VMAT-based focal radiotherapy approaches. METHODS AND MATERIALS: We identified 20 patients with one to ten brain metastases and at least one lesion larger than 15 cm3 who had received WBRT as part of routine care. For each patient, we designed and evaluated five radiotherapy treatment plans, including WBRT, HS-WBRT and three VMAT dosing models. A dose of 20 Gy in 5 fractions was prescribed to the whole brain or target volumes depending on the plan, with higher doses to smaller lesions and dose-escalated inner planning target volumes (DE-iPTV) in VMAT plans, respectively. Treatment plans were evaluated using the efficiency index, mean dose and D0.1 cc to the target volumes and organs at risk. RESULTS: Compared with WBRT, VMAT plans achieved a significantly more efficient dose distribution in brain lesions, especially with our DE-iPTV model, while minimising the dose to the normal brain and other organs at risks (OARs) (p < 0.05). CONCLUSIONS: VMAT plans obtained higher doses to brain metastases and minimised doses to OARs. Dose-escalated VMAT for larger lesions allows higher radiotherapy doses to be delivered to larger lesions while maintaining safe doses to OARs.

Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma.

BACKGROUND: Glioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of temporalis muscle, a surrogate for skeletal muscle mass, and assess its prognostic value in glioblastoma. METHODS: A neural network for temporalis segmentation was trained with 366 MRI head images from 132 patients from 4 different glioblastoma data sets and used to quantify muscle cross-sectional area (CSA). Association between temporalis CSA and survival was determined in 96 glioblastoma patients from internal and external data sets. RESULTS: The model achieved high segmentation accuracy (Dice coefficient 0.893). Median age was 55 and 58 years and 75.6 and 64.7% were males in the in-house and TCGA-GBM data sets, respectively. CSA was an independently significant predictor for survival in both the in-house and TCGA-GBM data sets (HR 0.464, 95% CI 0.218-0.988, p = 0.046; HR 0.466, 95% CI 0.235-0.925, p = 0.029, respectively). CONCLUSIONS: Temporalis CSA is a prognostic marker in patients with glioblastoma, rapidly and accurately assessable with deep learning. We are the first to show that a head/neck muscle-derived sarcopenia metric generated using deep learning is associated with oncological outcomes and one of the first to show deep learning-based muscle quantification has prognostic value in cancer.

A qualitative systematic review and thematic synthesis exploring the impacts of clinical academic activity by healthcare professionals outside medicine.

BACKGROUND: There are increasing opportunities for healthcare professionals outside medicine to be involved in and lead clinical research. However, there are few roles within these professions that include time for research. In order to develop such roles, and evaluate effective use of this time, the range of impacts of this clinical academic activity need to be valued and understood by healthcare leaders and managers. To date, these impacts have not been comprehensively explored, but are suggested to extend beyond traditional quantitative impact metrics, such as publications, citations and funding awards. METHODS: Ten databases, four grey literature repositories and a naïve web search engine were systematically searched for articles reporting impacts of clinical academic activity by healthcare professionals outside medicine. Specifically, this did not include the direct impacts of the research findings, rather the impacts of the research activity. All stages of the review were performed by a minimum of two reviewers and reported impacts were categorised qualitatively according to a modified VICTOR (making Visible the ImpaCT Of Research) framework. RESULTS: Of the initial 2704 identified articles, 20 were eligible for inclusion. Identified impacts were mapped to seven themes: impacts for patients; impacts for the service provision and workforce; impacts to research profile, culture and capacity; economic impacts; impacts on staff recruitment and retention; impacts to knowledge exchange; and impacts to the clinical academic. CONCLUSIONS: Several overlapping sub-themes were identified across the main themes. These included the challenges and benefits of balancing clinical and academic roles, the creation and implementation of new evidence, and the development of collaborations and networks. These may be key areas for organisations to explore when looking to support and increase academic activity among healthcare professionals outside medicine. The modified VICTOR tool is a useful starting point for individuals and organisations to record the impact of their research activity. Further work is needed to explore standardised methods of capturing research impact that address the full range of impacts identified in this systematic review and are specific to the context of clinical academics outside medicine.