Improving outcomes for women aged 70 years or above with early breast cancer: research programme including a cluster RCT

Background In breast cancer management, age-related practice variation is widespread, with older women having lower rates of surgery and chemotherapy than younger women, based on the premise of reduced treatment tolerance and benefit. This may contribute to inferior outcomes. There are currently no age- and fitness-stratified guidelines on which to base treatment recommendations. Aim We aimed to optimise treatment choice and outcomes for older women (aged ≥ 70 years) with operable breast cancer. Objectives Our objectives were to (1) determine the age, comorbidity, frailty, disease stage and biology thresholds for endocrine therapy alone versus surgery plus adjuvant endocrine therapy, or adjuvant chemotherapy versus no chemotherapy, for older women with breast cancer; (2) optimise survival outcomes for older women by improving the quality of treatment decision-making; (3) develop and evaluate a decision support intervention to enhance shared decision-making; and (4) determine the degree and causes of treatment variation between UK breast units. Design A prospective cohort study was used to determine age and fitness thresholds for treatment allocation. Mixed-methods research was used to determine the information needs of older women to develop a decision support intervention. A cluster-randomised trial was used to evaluate the impact of this decision support intervention on treatment choices and outcomes. Health economic analysis was used to evaluate the cost–benefit ratio of different treatment strategies according to age and fitness criteria. A mixed-methods study was used to determine the degree and causes of variation in treatment allocation. Main outcome measures The main outcome measures were enhanced age- and fitness-specific decision support leading to improved quality-of-life outcomes in older women (aged ≥ 70 years) with early breast cancer

Oncology-Led Early Identification of Nutritional Risk: A Pragmatic, Evidence-Based Protocol (PRONTO)

Nutritional issues, including malnutrition, low muscle mass, sarcopenia (i.e., low muscle mass and strength), and cachexia (i.e., weight loss characterized by a continuous decline in skeletal muscle mass, with or without fat loss), are commonly experienced by patients with cancer at all stages of disease. Cancer cachexia may be associated with poor nutritional status and can compromise a patient’s ability to tolerate antineoplastic therapy, increase the likelihood of post-surgical complications, and impact long-term outcomes including survival, quality of life, and function. One of the primary nutritional problems these patients experience is malnutrition, of which muscle depletion represents a clinically relevant feature. There have been recent calls for nutritional screening, assessment, treatment, and monitoring as a consistent component of care for all patients diagnosed with cancer. To achieve this, there is a need for a standardized approach to enable oncologists to identify patients commencing and undergoing antineoplastic therapy who are or who may be at risk of malnutrition and/or muscle depletion. This approach should not replace existing tools used in the dietitian’s role, but rather give the oncologist a simple nutritional protocol for optimization of the patient care pathway where this is needed. Given the considerable time constraints in day-to-day oncology practice, any such approach must be simple and quick to implement so that oncologists can flag individual patients for further evaluation and follow-up with appropriate members of the multidisciplinary care team. To enable the rapid and routine identification of patients with or at risk of malnutrition and/or muscle depletion, an expert panel of nutrition specialists and practicing oncologists developed the PROtocol for NuTritional risk in Oncology (PRONTO). The protocol enables the rapid identification of patients with or at risk of malnutrition and/or muscle depletion and provides guidance on next steps. The protocol is adaptable to multiple settings and countries, which makes implementation feasible by oncologists and may optimize patient outcomes. We advise the use of this protocol in countries/clinical scenarios where a specialized approach to nutrition assessment and care is not available

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

The DUNE Far Detector Vertical Drift Technology, Technical Design Report

International audienceDUNE is an international experiment dedicated to addressing some of the questions at the forefront of particle physics and astrophysics, including the mystifying preponderance of matter over antimatter in the early universe. The dual-site experiment will employ an intense neutrino beam focused on a near and a far detector as it aims to determine the neutrino mass hierarchy and to make high-precision measurements of the PMNS matrix parameters, including the CP-violating phase. It will also stand ready to observe supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector implements liquid argon time-projection chamber (LArTPC) technology, and combines the many tens-of-kiloton fiducial mass necessary for rare event searches with the sub-centimeter spatial resolution required to image those events with high precision. The addition of a photon detection system enhances physics capabilities for all DUNE physics drivers and opens prospects for further physics explorations. Given its size, the far detector will be implemented as a set of modules, with LArTPC designs that differ from one another as newer technologies arise. In the vertical drift LArTPC design, a horizontal cathode bisects the detector, creating two stacked drift volumes in which ionization charges drift towards anodes at either the top or bottom. The anodes are composed of perforated PCB layers with conductive strips, enabling reconstruction in 3D. Light-trap-style photon detection modules are placed both on the cryostat's side walls and on the central cathode where they are optically powered. This Technical Design Report describes in detail the technical implementations of each subsystem of this LArTPC that, together with the other far detector modules and the near detector, will enable DUNE to achieve its physics goals

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

The DUNE Far Detector Vertical Drift Technology, Technical Design Report

International audienceDUNE is an international experiment dedicated to addressing some of the questions at the forefront of particle physics and astrophysics, including the mystifying preponderance of matter over antimatter in the early universe. The dual-site experiment will employ an intense neutrino beam focused on a near and a far detector as it aims to determine the neutrino mass hierarchy and to make high-precision measurements of the PMNS matrix parameters, including the CP-violating phase. It will also stand ready to observe supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector implements liquid argon time-projection chamber (LArTPC) technology, and combines the many tens-of-kiloton fiducial mass necessary for rare event searches with the sub-centimeter spatial resolution required to image those events with high precision. The addition of a photon detection system enhances physics capabilities for all DUNE physics drivers and opens prospects for further physics explorations. Given its size, the far detector will be implemented as a set of modules, with LArTPC designs that differ from one another as newer technologies arise. In the vertical drift LArTPC design, a horizontal cathode bisects the detector, creating two stacked drift volumes in which ionization charges drift towards anodes at either the top or bottom. The anodes are composed of perforated PCB layers with conductive strips, enabling reconstruction in 3D. Light-trap-style photon detection modules are placed both on the cryostat's side walls and on the central cathode where they are optically powered. This Technical Design Report describes in detail the technical implementations of each subsystem of this LArTPC that, together with the other far detector modules and the near detector, will enable DUNE to achieve its physics goals