Mount Vernon Hospital is located in Northwood, an area of north-west Greater London. It is one of two hospitals run by The Hillingdon Hospitals NHS Foundation Trust.
To evaluate whether a two-step, ‘stage-gated’ reporting approach could improve the positive predictive value (PPV) of biparametric (bp)MRI for prostate cancer (PCa) screening compared to conventional Likert/PI-RADS scoring. This retrospective secondary analysis utilised data from IP1-PROSTAGRAM—a prospective, population-based study of men aged 50–69 years who underwent PCa screening with bpMRI, ultrasound and prostate-specific antigen (PSA) testing between October 2018 and May 2019 at two centres (NCT03702439). MRI scans from IP1-PROSTAGRAM were retrospectively evaluated using the ‘stage-gated’ approach: three radiologists independently reviewed limited MRI sequences (axial T2-weighted and b1500 diffusion-weighted images) and classified scans as positive or negative; if positive, the remaining bpMRI images were reviewed and a hypothetical “decision-to-biopsy” made. The PPV of ‘stage-gated’ reading was compared to PI-RADS and Likert scores ≥ 4 from the original IP1-PROSTAGRAM bpMRI reports. The reference standard was IP1-PROSTAGRAM biopsy results with grade group (GG) ≥ 2 cancer considered significant. Of 408 participants (median age 57 years [IQR 53, 61]), 405 had MRI scans available for secondary analysis. The prevalence of GG ≥ 2 cancer was 4
To develop and retrospectively validate an artificial intelligence-based decision support system (AI-DSS) for optimising prostate biopsy decisions and improving benefit-to-harm ratios. This retrospective, multicentre, multiscanner study used data from 1022 patients. An AI-DSS integrating PI-RADS scores, automated prostate-specific antigen density (PSAd), and deep-learning imaging risk scores was developed on 770 cases and validated on an independent cohort of 252 men from six UK centres. The AI-DSS performance was benchmarked against the real-world clinical decisions (reference standard) using grade selectivity, biopsy efficiency, and selective biopsy avoidance as outcome measures. Biopsy-proven detection of grade group (GG) ≥ 2 disease was the reference standard. In the validation cohort of 252 patients (mean age, 67.3 years), 137 underwent biopsy and 79 (31
AIMS:There is increasing interest in the potential benefits of reirradiation of recurrent or new primary cancer close to or within a previously irradiated region but there is a need for high-quality studies to evaluate this approach. This study aimed to understand clinician and NHS commissioner perspectives, methods for evidence generation, and the potential role of advanced technologies. MATERIALS AND METHODS:Semi-structured interviews were conducted with UK clinical oncologists and commissioners. Analysis was informed by principles of thematic analysis. RESULTS:Interviews were conducted and analysed with 10 clinical oncologists and 2 commissioners. Six themes were developed: 1. Weighing potential benefits against uncertainties; 2. Considering patient preferences in clinical decision-making; 3. A desire for clear guidance on planning and delivering reirradiation; 4. Challenges in the availability of reirradiation; 5. A desire for a stronger evidence-base to guide clinical care; 6. Considerations for clinical trial design. Clinicians and commissioners were supportive of strengthening the evidence base and increasing availability of reirradiation. Some clinicians and both commissioners favoured doing so using clinical trials whereas others considered prospective registries or evaluative commissioning to be more feasible. The potential benefits that could be gained from advanced technologies were highlighted, but also that many patients could benefit from high-quality standard photon reirradiation. CONCLUSION:This study provides important insights into reirradiation practice and how to improve its availability. Perspectives regarding trials and advanced technologies should shape future study design to strengthen the evidence base.
AIMS:There is increasing interest in the potential benefits of reirradiation for recurrent or new primary cancers close to or within a previously irradiated region, but there is a need for high-quality studies to evaluate this approach. This study aimed to understand patient and carer perspectives regarding reirradiation, future clinical trial design, and the potential role of advanced technologies. MATERIALS AND METHODS:Semi-structured interviews were conducted with patients who underwent reirradiation, and their carers. Patients were in follow-up and were approached about the study by their oncologist. Analysis was informed by principles of thematic analysis. RESULTS:Interviews involving 11 patients and 5 carers were conducted and analysed. Five themes were developed: 1. Considerable psychological impact from a diagnosis of recurrent cancer; 2. Influences on decision making for reirradiation; 3. Experience of reirradiation; 4. Considerations for future clinical trial design; 5. Considerations regarding advanced technologies in reirradiation. Patients and carers described the impact they had experienced from a recurrent cancer diagnosis, the strong influence of their treating oncologists on decision making regarding reirradiation, and absence of decision regret. In terms of future clinical trial design, study arms without reirradiation, especially if these contain no active treatment, may be less acceptable to patients. Some patients would be prepared to travel or temporarily relocate for reirradiation using advanced technologies such as proton beam therapy, but for others the family/social/financial impact would make this challenging. CONCLUSION:This study has provided important insights from patients and their carers regarding reirradiation for recurrent cancer. Perspectives regarding clinical trials and advanced technologies will help to shape future study design.
Purpose To provide an initial estimate of the carbon footprint of PET/CT imaging at a UK imaging centre, indicating the major contributing factors to the emission of greenhouse gases. Basic procedures Data were collected for patient and staff travel, energy use, consumables and waste from the PET/CT department at the Paul Strickland Scanner Centre (PSSC). Additional travel data from two other UK departments were used as a guide to ensure reasonable representation of UK travel distances. Data were converted to carbon dioxide equivalent emissions (kg CO2e) and expressed in terms of emissions per patient scanned. Main findings The carbon footprint of PET/CT imaging at PSSC was measured as 19 kg CO2e or 21 kg CO2e per patient, depending on the scanner used. The most significant contributors to this were patient travel, and the energy taken by the scanner (this includes the energy taken when the scanner is not operating). Conclusions Whilst 20 kg CO2e is not large by healthcare standards, it is not negligible, representing around 4% to 10% of the emissions of a typical course of radiotherapy. The findings demonstrate the areas of focus to reduce the footprint, and act as a base for future work. This is an initial estimate and does not include the footprint of production and distribution of the radiotracer, nor the “embedded footprint” of the imaging facility and equipment.
