Race-ethnic and gender differences in representation within the English National Health Service: a quantitative analysis

Objectives To evaluate race-ethnic and gender disparities in National Health Service (NHS) England employment in position, prestige and pay. Design National study using data from NHS Digital. Setting Trusts and clinical commissioning groups in England. Participants 1 105 390 NHS Hospital and Community Health Service staff. Results Chinese people (42.9%, 95% CI 41.7% to 44.1%) are the most likely to be employed as doctors, followed by Asians (28.6%, 95% CI 28.3% to 28.8%) and people of mixed race/ethnicity (17.9%, 95% CI 17.3% to 18.4%); while white people (6.8%, 95% CI 6.7% to 6.8%) are less likely to be employed as doctors. However, white doctors are the most likely to be in the highest paid positions: 46.0% (95% CI 45.6% to 46.4%) of white doctors are consultants, whereas only 33.4% (95% CI 31.6% to 35.2%) of Chinese doctors are consultants. Black people are under-represented both among doctors and as consultants: 6.5% (95% CI 6.4% to 6.7%) of black employees are doctors and 30.6% (95% CI 29.2% to 32.0%) of black doctors are consultants. We found similar results for nurses and health visitors, where white people are over-represented in the higher pay bands. However, among support staff for doctors, nurses and midwives, we found that Chinese people were over-represented in the higher pay bands. These race-ethnic differences were similar for women and men. Additionally, we found that men were more likely to be employed in higher pay bands than women, and this gender disparity was apparent across race-ethnic groups. Conclusions Race-ethnic and gender disparities exist in the NHS in position, prestige and pay. To begin to overcome such disparities, the NHS must collect data using consistent race-ethnic categories in order to examine differences over time.Department of Health and Social Care; NHS Englan

Hyperpolarised 13C MRI: a new horizon for non-invasive diagnosis of aggressive breast cancer

Hyperpolarised 13C MRI (HP-MRI) is a novel imaging technique that allows real-time analysis of metabolic pathways in vivo. 1 The technology to conduct HP-MRI in humans has recently become available and is starting to be clinically applied. As knowledge of molecular biology advances, it is increasingly apparent that cancer cell metabolism is related to disease outcomes, with lactate attracting specific attention. 2 Recent reviews of breast cancer screening programs have raised concerns and increased public awareness of over treatment. The scientific community needs to shift focus from improving cancer detection alone to pursuing novel methods of distinguishing aggressive breast cancers from those which will remain indolent. HP-MRI offers the opportunity to identify aggressive tumour phenotypes and help monitor/predict therapeutic response. Here we report one of the first cases of breast cancer imaged using HP-MRI alongside correlative conventional imaging, including breast MRI

A randomized trial of bevacizumab for newly diagnosed glioblastoma.

BACKGROUND: Concurrent treatment with temozolomide and radiotherapy followed by maintenance temozolomide is the standard of care for patients with newly diagnosed glioblastoma. Bevacizumab, a humanized monoclonal antibody against vascular endothelial growth factor A, is currently approved for recurrent glioblastoma. Whether the addition of bevacizumab would improve survival among patients with newly diagnosed glioblastoma is not known. METHODS: In this randomized, double-blind, placebo-controlled trial, we treated adults who had centrally confirmed glioblastoma with radiotherapy (60 Gy) and daily temozolomide. Treatment with bevacizumab or placebo began during week 4 of radiotherapy and was continued for up to 12 cycles of maintenance chemotherapy. At disease progression, the assigned treatment was revealed, and bevacizumab therapy could be initiated or continued. The trial was designed to detect a 25% reduction in the risk of death and a 30% reduction in the risk of progression or death, the two coprimary end points, with the addition of bevacizumab. RESULTS: A total of 978 patients were registered, and 637 underwent randomization. There was no significant difference in the duration of overall survival between the bevacizumab group and the placebo group (median, 15.7 and 16.1 months, respectively; hazard ratio for death in the bevacizumab group, 1.13). Progression-free survival was longer in the bevacizumab group (10.7 months vs. 7.3 months; hazard ratio for progression or death, 0.79). There were modest increases in rates of hypertension, thromboembolic events, intestinal perforation, and neutropenia in the bevacizumab group. Over time, an increased symptom burden, a worse quality of life, and a decline in neurocognitive function were more frequent in the bevacizumab group. CONCLUSIONS: First-line use of bevacizumab did not improve overall survival in patients with newly diagnosed glioblastoma. Progression-free survival was prolonged but did not reach the prespecified improvement target. (Funded by the National Cancer Institute; ClinicalTrials.gov number, NCT00884741.)

First steps towards a fast-neutron therapy planning program

<p>Abstract</p> <p>Background</p> <p>The Monte Carlo code GEANT4 was used to implement first steps towards a treatment planning program for fast-neutron therapy at the FRM II research reactor in Garching, Germany. Depth dose curves were calculated inside a water phantom using measured primary neutron and simulated primary photon spectra and compared with depth dose curves measured earlier. The calculations were performed with GEANT4 in two different ways, simulating a simple box geometry and splitting this box into millions of small voxels (this was done to validate the voxelisation procedure that was also used to voxelise the human body).</p> <p>Results</p> <p>In both cases, the dose distributions were very similar to those measured in the water phantom, up to a depth of 30 cm. In order to model the situation of patients treated at the FRM II MEDAPP therapy beamline for salivary gland tumors, a human voxel phantom was implemented in GEANT4 and irradiated with the implemented MEDAPP neutron and photon spectra. The 3D dose distribution calculated inside the head of the phantom was similar to the depth dose curves in the water phantom, with some differences that are explained by differences in elementary composition. The lateral dose distribution was studied at various depths. The calculated cumulative dose volume histograms for the voxel phantom show the exposure of organs at risk surrounding the tumor.</p> <p>Conclusions</p> <p>In order to minimize the dose to healthy tissue, a conformal treatment is necessary. This can only be accomplished with the help of an advanced treatment planning system like the one developed here. Although all calculations were done for absorbed dose only, any biological dose weighting can be implemented easily, to take into account the increased radiobiological effectiveness of neutrons compared to photons.</p

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Toward a standard for the evaluation of PET-Auto-Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation

Purpose: The aim of this paper is to define the requirements and describe the design and implementation of a standard benchmark tool for evaluation and validation of PET-auto-segmentation (PET-AS) algorithms. This work follows the recommendations of Task Group 211 (TG211) appointed by the American Association of Physicists in Medicine (AAPM).Methods: The recommendations published in the AAPM TG211 report were used to derive a set of required features and to guide the design and structure of a benchmarking software tool. These items included the selection of appropriate representative data and reference contours obtained from established approaches and the description of available metrics. The benchmark was designed in a way that it could be extendable by inclusion of bespoke segmentation methods, while maintaining its main purpose of being a standard testing platform for newly developed PET-AS methods. An example of implementation of the proposed framework, named PETASset, was built. In this work, a selection of PET-AS methods representing common approaches to PET image segmentation was evaluated within PETASset for the purpose of testing and demonstrating the capabilities of the software as a benchmark platform.Results: A selection of clinical, physical, and simulated phantom data, including "best estimates" reference contours from macroscopic specimens, simulation template, and CT scans was built into the PETASset application database. Specific metrics such as Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity (S), were included to allow the user to compare the results of any given PET-AS algorithm to the reference contours. In addition, a tool to generate structured reports on the evaluation of the performance of PET-AS algorithms against the reference contours was built. The variation of the metric agreement values with the reference contours across the PET-AS methods evaluated for demonstration were between 0.51 and 0.83, 0.44 and 0.86, and 0.61 and 1.00 for DSC, PPV, and the S metric, respectively. Examples of agreement limits were provided to show how the software could be used to evaluate a new algorithm against the existing state-of-the art.Conclusions: PETASset provides a platform that allows standardizing the evaluation and comparison of different PET-AS methods on a wide range of PET datasets. The developed platform will be available to users willing to evaluate their PET-AS methods and contribute with more evaluation datasets. </p

First-in-human in vivo non-invasive assessment of intra-tumoral metabolic heterogeneity in renal cell carcinoma

Intratumoral genetic heterogeneity and the role of metabolic reprogramming in renal cell carcinoma have been extensively documented. However, the distribution of these metabolic changes within the tissue has not been explored. We report on the first-in-human in vivo non-invasive metabolic interrogation of renal cell carcinoma using hyperpolarized carbon-13 (13C) MRI and describe the validation of in vivo lactate metabolic heterogeneity against multi regional ex vivo mass spectrometry. hyperpolarized carbon-13 (13C)-MRI provides an in vivo assessment of metabolism and provides a novel opportunity to safely and non-invasively assess cancer heterogeneity