Defining the road map to a UK national lung cancer screening programme.

Lung cancer screening with low-dose CT was recommended by the UK National Screening Committee (UKNSC) in September, 2022, on the basis of data from trials showing a reduction in lung cancer mortality. These trials provide sufficient evidence to show clinical efficacy, but further work is needed to prove deliverability in preparation for a national roll-out of the first major targeted screening programme. The UK has been world leading in addressing logistical issues with lung cancer screening through clinical trials, implementation pilots, and the National Health Service (NHS) England Targeted Lung Health Check Programme. In this Policy Review, we describe the consensus reached by a multiprofessional group of experts in lung cancer screening on the key requirements and priorities for effective implementation of a programme. We summarise the output from a round-table meeting of clinicians, behavioural scientists, stakeholder organisations, and representatives from NHS England, the UKNSC, and the four UK nations. This Policy Review will be an important tool in the ongoing expansion and evolution of an already successful programme, and provides a summary of UK expert opinion for consideration by those organising and delivering lung cancer screenings in other countries.

Recommendations for the development and use of imaging test sets to investigate the test performance of artificial intelligence in health screening.

Rigorous evaluation of artificial intelligence (AI) systems for image classification is essential before deployment into health-care settings, such as screening programmes, so that adoption is effective and safe. A key step in the evaluation process is the external validation of diagnostic performance using a test set of images. We conducted a rapid literature review on methods to develop test sets, published from 2012 to 2020, in English. Using thematic analysis, we mapped themes and coded the principles using the Population, Intervention, and Comparator or Reference standard, Outcome, and Study design framework. A group of screening and AI experts assessed the evidence-based principles for completeness and provided further considerations. From the final 15 principles recommended here, five affect population, one intervention, two comparator, one reference standard, and one both reference standard and comparator. Finally, four are appliable to outcome and one to study design. Principles from the literature were useful to address biases from AI; however, they did not account for screening specific biases, which we now incorporate. The principles set out here should be used to support the development and use of test sets for studies that assess the accuracy of AI within screening programmes, to ensure they are fit for purpose and minimise bias.

UK National Screening Committee's approach to reviewing evidence on artificial intelligence in breast cancer screening.

Artificial intelligence (AI) could have the potential to accurately classify mammograms according to the presence or absence of radiological signs of breast cancer, replacing or supplementing human readers (radiologists). The UK National Screening Committee's assessments of the use of AI systems to examine screening mammograms continues to focus on maximising benefits and minimising harms to women screened, when deciding whether to recommend the implementation of AI into the Breast Screening Programme in the UK. Maintaining or improving programme specificity is important to minimise anxiety from false positive results. When considering cancer detection, AI test sensitivity alone is not sufficiently informative, and additional information on the spectrum of disease detected and interval cancers is crucial to better understand the benefits and harms of screening. Although large retrospective studies might provide useful evidence by directly comparing test accuracy and spectrum of disease detected between different AI systems and by population subgroup, most retrospective studies are biased due to differential verification (ie, the use of different reference standards to verify the target condition among study participants). Enriched, multiple-reader, multiple-case, test set laboratory studies are also biased due to the laboratory effect (ie, radiologists' performance in retrospective, laboratory, observer studies is substantially different to their performance in a clinical environment). Therefore, assessment of the effect of incorporating any AI system into the breast screening pathway in prospective studies is required as it will provide key evidence for the effect of the interaction of medical staff with AI, and the impact on women's outcomes.

The projected impact of the COVID-19 lockdown on breast cancer deaths in England due to the cessation of population screening: a national estimation.

BACKGROUND: Population breast screening services in England were suspended in March 2020 due to the COVID-19 pandemic. Here, we estimate the number of breast cancers whose detection may be delayed because of the suspension, and the potential impact on cancer deaths over 10 years. METHODS: We estimated the number and length of screening delays from observed NHS Breast Screening System data. We then estimated additional breast cancer deaths from three routes: asymptomatic tumours progressing to symptomatically diagnosed disease, invasive tumours which remain screen-detected but at a later date, and ductal carcinoma in situ (DCIS) progressing to invasive disease by detection. We took progression rates, prognostic characteristics, and survival rates from published sources. RESULTS: We estimated that 1,489,237 women had screening delayed by around 2-7 months between July 2020 and June 2021, leaving 745,277 outstanding screens. Depending on how quickly this backlog is cleared, around 2500-4100 cancers would shift from screen-detected to symptomatic cancers, resulting in 148-452 additional breast cancer deaths. There would be an additional 164-222 screen-detected tumour deaths, and 71-97 deaths from DCIS that progresses to invasive cancer. CONCLUSIONS: An estimated 148-687 additional breast cancer deaths may occur as a result of the pandemic-related disruptions. The impact depends on how quickly screening services catch up with delays.

The Tn antigen-specific lectin from ground ivy is an insecticidal protein with an unusual physiology.

Leaves of ground ivy (Glechoma hederacea) contain a lectin (called Gleheda) that is structurally and evolutionary related to the classical legume lectins. Screening of a population of wild plants revealed that Gleheda accounts for more than one-third of the total leaf protein in some clones, whereas it cannot be detected in other clones growing in the same environment. Gleheda is predominantly expressed in the leaves where it accumulates during early leaf maturation. The lectin is not uniformly distributed over the leaves but exhibits a unique localization pattern characterized by an almost exclusive confinement to a single layer of palisade parenchyma cells. Insect feeding trials demonstrated that Gleheda is a potent insecticidal protein for larvae of the Colorado potato beetle (Leptinotarsa decemlineata). Because Gleheda is not cytotoxic, it is suggested that the insecticidal activity is linked to the carbohydrate-binding specificity of the lectin, which as could be demonstrated by agglutination assays with different types of polyagglutinable human erythrocytes is specifically directed against the Tn antigen structure (N-acetylgalactosamine O-linked to serine or threonine residues of proteins).