6-20-2, Shinkawa, Mitaka, Japan

MIDI (MR Imaging Abnormality Deep Learning Identification)

An automated strategy for identifying abnormalities in head scans could address the unmet clinical need for faster abnormality identification times, potentially allowing for early intervention to improve short- and long-term clinical outcomes. Radiologist shortages and increased demand for MRI scans lead to delays in reporting, particularly in the outpatient setting. Furthermore, there is a wide variation in the management of incidental findings (IFs) discovered in 'healthy volunteers.' The routine reporting of 'healthy volunteer' scans by a radiologist poses logistical and financial challenges. It would be valuable to devise automated strategies to reliably and accurately identify IFs, potentially reducing the number of scans requiring routine radiological review by up to 90%, thus increasing the feasibility of implementing a routine reporting strategy. Deep learning is a novel technique in computer science that automatically learns hierarchies of relevant features directly from the raw inputs (such as MRI or CT) using multi-layered neural networks. A deep learning algorithm will be trained on a large database of head MRI scans to recognize scans with abnormalities. This algorithm will be trained to classify a subset of these scans as normal or abnormal and then tested on an independent subset to determine its validity. If the tested neural network demonstrates high diagnostic accuracy, future research participants and patients may benefit, as not all institutions currently review their research scans for incidental findings and clinical scans may not be reported for weeks in some cases. In both research and clinical scenarios, an algorithm could rapidly identify abnormal pathology and prioritize scans for reporting. In summary, the aim is to develop a deep learning abnormality detection algorithm for use in both research and clinical settings.

The Early Valve Replacement in Severe ASYmptomatic Aortic Stenosis Study

This is a major pragmatic multi-centre prospective parallel group open RCT. It will be conducted in the UK, Australia and New Zealand, funding is being sought in several countries to expand recruitment internationally. The study is in 2 phases: the vanguard and main phase. Therefore the study will run an internal pilot to prove recruitment of the relevant number of participants during the initial 2 years. The over-arching aim is to determine whether early AVR results in better clinical outcomes and cost-effectiveness than a strategy of expectant management in asymptomatic patients with severe AS. The primary hypothesis is that early AVR or TAVI in asymptomatic patients with severe AS will result in a reduction in the composite primary outcome of cardiovascular (CV) death and hospitalisation for heart failure (HHF) when compared to the conventional approach of expectant management. Potential participants will be identified by a member of the clinical care team following diagnosis with severe AS. Participants will be screened for eligibility using pre-specified inclusion/exclusion criteria. Eligible participants will be provided with a written version of the participant information sheet detailing the exact nature of the study, what it will involve for the participant and any risks involved with taking part. Participants will be given at least 24 hours to consider the information and decide whether or not to take part. The study will randomise up to 2844 patients with severe asymptomatic AS to either allocated expectant management OR aortic valve replacement. Participants randomised to AVR will be placed on a waiting list with the aim that surgery will be performed within 3 months, dependent on local hospitals' waiting lists. Participants randomised to AVR will undergo routine tests/procedures which may include coronary angiography. If the outcome of the coronary angiography reveals coronary heart disease, the decision to perform CABG or PCI will be made by the responsible cardiac surgeon and cardiologist, in consultation with the patient. All analyses will be undertaken using the principles of intention-to-treat with participants analysed in the group they were randomised regardless of treatment received. EASY-AS is collaborating with the EVoLVeD study (Early Valve Replacement guided by Biomarkers of Left Ventricular Decompensation in Asymptomatic Patients with Severe Aortic Stenosis, Clinical Trials.gov NCT03094143). In centres where both EASY-AS and EVoLVeD are running, participants in EASY-AS will be offered the opportunity to take part in EVoLVeD. Funding has been granted by the British Heart Foundation (UK), Medical Research Future Fund (Aus) and Heart Foundation (NZ). The UK sponsor is the University of Leicester. Additional support and resources for the study will be provided by the participating Trusts and their corresponding Clinical Research Networks in the UK. The central co-ordination centre is the University of Leicester Clinical Trials Unit.

Renal Adjuvant MultiPle Arm Randomised Trial

The Role of Ixazomib in Autologous Stem Cell Transplant in Relapsed Myeloma - Myeloma XII (ACCoRd)

Myeloma XIV: Frailty-adjusted Therapy in Transplant Non-Eligible Patients With Newly Diagnosed Multiple Myeloma

Randomised Evaluation of Sodium Dialysate Levels on Vascular Events

RESOLVE is a pragmatic, cluster-randomised, open-label study designed to evaluate in real-world conditions the comparative effectiveness of two default dialysate sodium concentrations. Dialysis sites will be randomised in a 1:1 ratio to a default dialysate sodium concentration of 137mmol/l or 140mmol/l. 'Default' is defined as the use of the allocated dialysate sodium for ≥ 90% of delivered dialysis sessions in the unit. All other care will be according to standard local practices as determined by the site. Outcomes will be assessed on individual patients dialysing at those sites. Sites will be asked to consent to participation while waiver or opt-out consent will be sought for individual patients. It is anticipated that site accrual will occur over 5-7 years with average study duration expected to be approximately 2-5 years. The actual length of the study will be end-point determined.

A Study for the Participants With Metastatic Hormone Sensitive Prostate Cancer (mHSPC) Treated With Androgen Deprivation Therapy (ADT) Plus Apalutamide or Enzalutamide