Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study.

Background: Cardiovascular disease remains a leading cause of morbidity and mortality worldwide. This series of systematic reviews and meta-analyses synthesised evidence on the effectiveness, comparative effectiveness and cost-effectiveness of pharmacological and non-pharmacological interventions for primary cardiovascular disease prevention.

Methods: Five systematic reviews and meta-analyses were conducted using rigorous methods, including comprehensive searches, duplicate screening, risk-of-bias assessments and adherence to reporting guidelines.

LATE-STAGE OUTCOMES AS SURROGATES FOR MORTALITY IN CANCER SCREENING TRIALS: A SYSTEMATIC REVIEW AND META-ANALYSIS.

Late-stage cancer incidence has been proposed as a surrogate outcome for cancer-specific mortality in future screening trials. Two previous meta-analyses with 33 and 39 trials assessed trial-level surrogacy but provided inconsistent conclusions about the suitability of late-stage cancer endpoints replacing mortality. Our systematic review and meta-analysis (PROSPERO, CRD42023369320) investigated the association between the effect of cancer screening on incidence of late-stage cancer and cancer-specific mortality.

Identifying optimal primary prevention interventions for major cardiovascular disease events and all-cause mortality: a systematic review and hierarchical network meta-analysis of RCTs.

Background: Cardiovascular disease accounts for substantial mortality and healthcare costs worldwide. Numerous interventions exist for primary prevention but lack head-to-head comparisons on long-term impacts.

Objective: To determine the comparative effectiveness of interventions for primary cardiovascular disease prevention through network meta-analysis of randomised trials.

Target product profiles for digital health technologies including those with artificial intelligence: a systematic review.

Digital health technologies (DHTs), including those incorporating artificial intelligence (AI), have the potential to improve healthcare access, efficiency, and quality, reducing gaps between healthcare capacity and demand. Despite prioritisation in health policy, the adoption of DHTs remains limited, especially for AI, in part due to complex system requirements. Target product profiles (TPPs) are documents outlining the characteristics necessary for medical technologies to be utilised in practice and offer a way to align DHTs' research and development with health systems' needs.

Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation.

Background: Lung cancer is one of the most common types of cancer and the leading cause of cancer death in the United Kingdom. Artificial intelligence-based software has been developed to reduce the number of missed or misdiagnosed lung nodules on computed tomography images.

Objective:  To assess the accuracy, clinical effectiveness and cost-effectiveness of using  software with artificial intelligence-derived algorithms to assist in the detection and analysis of lung nodules in computed tomography scans of the chest compared with unassisted reading.

SMALL: open surgery versus minimally invasive vacuum-assisted excision for small screen-detected breast cancer-protocol for a phase III randomised multicentre trial.

Introduction: Mammographic screening identifies many women with small breast cancers with favourable biological features, which have an excellent prognosis. Some of these may never have become clinically apparent without screening and are commonly described as 'overdiagnosed' cancers. Despite this, all patients with screen-detected cancers are currently treated with surgical excision and sentinel lymph node biopsy, although this may represent overtreatment.

Risk stratification in breast screening workshop.

Population screening for breast cancer (BC) is currently offered in the UK for women aged 50 to 71 with the aim of reducing mortality. There is additional screening within the national programme for women identified as having a very high risk of BC. There is growing interest in further risk stratification in breast screening, which would require a whole population risk assessment and the subsequent offer of screening tailored to the individual's risk.

Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies.

Objectives: To examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT.

Methods: A systematic review of CE-marked, AI-based software for automated detection and analysis of nodules in CT lung cancer screening was conducted. Multiple databases including Medline, Embase and Cochrane CENTRAL were searched from 2012 to March 2023.

Modeling Radiologists' Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms.

Purpose: To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance.

Methods: Logistic regression models were designed and used to model individual radiologist assessments. For model evaluation, model-predicted individual performance metrics and paired disagreement rates were compared against the observed data using Pearson correlation coefficients.

Interventions for primary prevention of cardiovascular disease: umbrella review of systematic reviews.

Background: Cardiovascular diseases are the leading cause of death globally. The aim of this overview of systematic reviews was to compare the effectiveness of different pharmacological and non-pharmacological interventions for the primary prevention of cardiovascular disease.

Methods: A structured search of the Cochrane Database of Systematic Reviews, MEDLINE, EMBASE and the Database of Abstracts of Reviews of Effects archive was conducted to find systematic reviews that reported the effect of various pharmacological and non-pharmacological interventions for the primary prevention of cardiovascular disease from inception to March 2021.

Benefits and harms of antenatal and newborn screening programmes in health economic assessments: the VALENTIA systematic review and qualitative investigation.

Background: Health economic assessments are used to determine whether the resources needed to generate net benefit from an antenatal or newborn screening programme, driven by multiple benefits and harms, are justifiable. It is not known what benefits and harms have been adopted by economic evaluations assessing these programmes and whether they omit benefits and harms considered important to relevant stakeholders.

Objectives: (1) To identify the benefits and harms adopted by health economic assessments in this area, and to assess how they have been measured and valued; (2) to identify attributes or relevance to stakeholders that ought to be considered in future economic assessments; and (3) to make recommendations about the benefits and harms that should be considered by these studies.

Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study.

Background: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation.

Fatigue and vigilance in medical experts detecting breast cancer.

An abundance of laboratory-based experiments has described a vigilance decrement of reducing accuracy to detect targets with time on task, but there are few real-world studies, none of which have previously controlled the environment to control for bias. We describe accuracy in clinical practice for 360 experts who examined >1 million women's mammograms for signs of cancer, whilst controlling for potential biases. The vigilance decrement pattern was not observed.

Atypia detected during breast screening and subsequent development of cancer: observational analysis of the Sloane atypia prospective cohort in England.

Objective: To explore how the number and type of breast cancers developed after screen detected atypia compare with the anticipated 11.3 cancers detected per 1000 women screened within one three year screening round in the United Kingdom.

Design: Observational analysis of the Sloane atypia prospective cohort in England.

Evidence-informed recommendations on managing breast screening atypia: perspectives from an expert panel consensus meeting reviewing results from the Sloane atypia project.

Evidence-based clinical guidelines are essential to maximize patient benefit and to reduce clinical uncertainty and inconsistency in clinical practice. Gaps in the evidence base can be addressed by data acquired in routine practice. At present, there is no international consensus on management of women diagnosed with atypical lesions in breast screening programmes.

Development and quality appraisal of a new English breast screening linked data set as part of the age, test threshold, and frequency of mammography screening (ATHENA-M) study.

Objectives: To build a data set capturing the whole breast cancer screening journey from individual breast cancer screening records to outcomes and assess data quality.

Methods: Routine screening records (invitation, attendance, test results) from all 79 English NHS breast screening centres between January 1, 1988 and March 31, 2018 were linked to cancer registry (cancer characteristics and treatment) and national mortality data. Data quality was assessed using comparability, validity, timeliness, and completeness.

The potential impact of policies and structural interventions in reducing cardiovascular disease and mortality: a systematic review of simulation-based studies.

Background: The aim of the study was to investigate the potential effect of different structural interventions for preventing cardiovascular disease.

Methods: Medline and EMBASE were searched for peer-reviewed simulation-based studies of structural interventions for prevention of cardiovascular disease. We performed a systematic narrative synthesis.

Special Section Editorial: Artificial Intelligence for Medical Imaging in Clinical Practice.

The editorial introduces the JMI Special Section on Artificial Intelligence for Medical Imaging in Clinical Practice.

Optimizing the Pairs of Radiologists That Double Read Screening Mammograms.

Background Despite variation in performance characteristics among radiologists, the pairing of radiologists for the double reading of screening mammograms is performed randomly. It is unknown how to optimize pairing to improve screening performance. Purpose To investigate whether radiologist performance characteristics can be used to determine the optimal set of pairs of radiologists to double read screening mammograms for improved accuracy.

Interobserver variability studies in diagnostic imaging: a methodological systematic review.

Objectives: To review the methodology of interobserver variability studies; including current practice and quality of conducting and reporting studies.

Methods: Interobserver variability studies between January 2019 and January 2020 were included; extracted data comprised of study characteristics, populations, variability measures, key results, and conclusions. Risk of bias was assessed using the COSMIN tool for assessing reliability and measurement error.

Increasing comprehensiveness and reducing workload in a systematic review of complex interventions using automated machine learning.

Background: As part of our ongoing systematic review of complex interventions for the primary prevention of cardiovascular diseases, we have developed and evaluated automated machine-learning classifiers for title and abstract screening. The aim was to develop a high-performing algorithm comparable to human screening.

Methods: We followed a three-phase process to develop and test an automated machine learning-based classifier for screening potential studies on interventions for primary prevention of cardiovascular disease.