Acceptability of the Cytosponge procedure for detecting Barrett’s oesophagus: A qualitative study

Objective: To investigate the acceptability of the Cytosponge, a novel sampling device to detect Barrett's oesophagus (BE), a precursor to oesophageal adenocarcinoma (EAC), among people with risk factors for this condition. Design: A qualitative study using semistructured interviews and focus group discussions. Data were explored by three researchers using thematic analysis. Setting: Community setting in London, UK. Participants: A recruitment company identified 33 adults (17 men, 16 women) aged 50–69 years with gastro-oesophageal reflux disease (GERD), a risk factor for BE. The majority of participants were white British (73%). The focus groups were stratified by gender and education. 10 individuals were interviewed and 23 participated in four focus groups. Results: 3 key themes emerged from the data: the anticipated physical experience, preferences for the content of information materials and comparisons with the current gold-standard test. Overall acceptability was high, but there was initial concern about the physical experience of taking the test, including swallowing and extracting the Cytosponge. These worries were reduced after handling the device and a video demonstration of the procedure. Knowledge of the relationship between GERD, BE and EAC was poor, and some suggested they would prefer not to know about the link when being offered the Cytosponge. Participants perceived the Cytosponge to be more comfortable, practical and economical than endoscopy. Conclusions: These qualitative data suggest the Cytosponge was acceptable to the majority of participants with risk factors for BE, and could be used as a first-line test to investigate GERD symptoms. Concerns about the physical experience of the test were alleviated through multimedia resources. The development of patient information materials is an important next step to ensuring patients are adequately informed and reassured about the procedure. Patient stakeholders should be involved in this process to ensure their concerns and preferences are considered

Mammographic density and its interaction with other breast cancer risk factors in an Asian population

10.1038/sj.bjc.6606085British Journal of Cancer1045871-874BJCA

Effect of second timed appointments for non-attenders of breast cancer screening in England : a randomised controlled trial

BACKGROUND: In England, participation in breast cancer screening has been decreasing in the past 10 years, approaching the national minimum standard of 70%. Interventions aimed at improving participation need to be investigated and put into practice to stop this downward trend. We assessed the effect on participation of sending invitations for breast screening with a timed appointment to women who did not attend their first offered appointment within the NHS Breast Screening Programme (NHSBSP). METHODS: In this open, randomised controlled trial, women in six centres in the NHSBSP in England who were invited for routine breast cancer screening were randomly assigned (1:1) to receive an invitation to a second appointment with fixed date and time (intervention) or an invitation letter with a telephone number to call to book their new screening appointment (control) in the event of non-attendance at the first offered appointment. Randomisation was by SX number, a sequential unique identifier of each woman within the NHSBSP, and at the beginning of the study a coin toss decided whether women with odd or even SX numbers would be allocated to the intervention group. Women aged 50-70 years who did not attend their first offered appointment were eligible for the analysis. The primary endpoint was participation (ie, attendance at breast cancer screening) within 90 days of the date of the first offered appointment; we used Poisson regression to compare the proportion of women who participated in screening in the study groups. All analyses were by intention to treat. This trial is registered with Barts Health, number 009304QM. FINDINGS: We obtained 33 146 records of women invited for breast cancer screening at the six centres between June 2, 2014, and Sept 30, 2015, who did not attend their first offered appointment. 26 054 women were eligible for this analysis (12 807 in the intervention group and 13 247 in the control group). Participation within 90 days of the first offered appointment was significantly higher in the intervention group (2861 [22%] of 12 807) than in the control group (1632 [12%] of 13 247); relative risk of participation 1·81 (95% CI 1·70-1·93; p<0·0001). INTERPRETATION: These findings show that a policy of second appointments with fixed date and time for non-attenders of breast screening is effective in improving participation. This strategy can be easily implemented by the screening sites and, if combined with simple interventions, could further increase participation and ensure an upward shift in the participation trend nationally. Whether the policy should vary by time since last attended screen will have to be considered. FUNDING: National Health Service Cancer Screening Programmes and Department of Health Policy Research Programme

MesoGraph: automatic profiling of mesothelioma subtypes from histological images

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89±0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS

International burden of cancer deaths and years of life lost from cancer attributable to four major risk factors: a population-based study in Brazil, Russia, India, China, South Africa, the United Kingdom, and United States

Background: We provide a comprehensive view of the impact of alcohol consumption, tobacco smoking, excess body weight, and human papillomavirus (HPV) infection on cancer mortality and years of life lost (YLLs) in Brazil, Russia, India, China, South Africa, the United Kingdom (UK), and United States (US). Methods: We collected population attributable fractions of the four risk factors from global population-based studies and applied these to estimates of cancer deaths in 2020 to obtain potentially preventable cancer deaths and their 95% confidence intervals (CIs). Using life tables, we calculated the number and age-standardised rates of YLLs (ASYR). Findings: In Brazil, Russia, India, China, South Africa, the UK, and the US in 2020, an estimated 5.9 million (3.3 million–8.6 million) YLLs from cancer were attributable to alcohol consumption, 20.8 million (17.0 million–24.6 million) YLLs to tobacco smoking, 3.1 million (2.4 million–3.8 million) YLLs to excess body weight, and 4.0 million (3.9 million–4.2 million) YLLs to HPV infection. The ASYR from cancer due to alcohol consumption was highest in China (351.4 YLLs per 100,000 population [95% CI 194.5–519.2]) and lowest in the US (113.5 [69.6–157.1]) and India (115.4 [49.7–172.7). For tobacco smoking, China (1159.9 [950.6–1361.8]) had the highest ASYR followed by Russia (996.8 [831.0–1154.5). For excess body weight, Russia and the US had the highest ASYRs (385.1 [280.6–481.2] and 369.4 [299.6–433.6], respectively). The highest ASYR due to HPV infection was in South Africa (457.1 [453.3–462.6]). ASYRs for alcohol consumption and tobacco smoking were higher among men than women, whereas women had higher ASYRs for excess body weight and HPV infection. Interpretation: Our findings demonstrate the importance of cancer control efforts to reduce the burden of cancer death and YLLs due to modifiable cancer risk factors and promote the use of YLLs to summarise disease burden. Funding: Cancer Research UK

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89 ± 0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS

The cost-effectiveness of risk stratified breast cancer screening in the UK

Background There has been growing interest in the UK and internationally of risk-stratified breast screening whereby individualised risk assessment may inform screening frequency, starting age, screening instrument used, or even decisions not to screen. This study evaluates the cost-effectiveness of eight proposals for risk-stratified screening regimens compared to both the current UK screening programme and no national screening. Methods A person-level microsimulation model was developed to estimate health-related quality of life, cancer survival and NHS costs over the lifetime of the female population eligible for screening in the UK. Results Compared with both the current screening programme and no screening, risk-stratified regimens generated additional costs and QALYs, and had a larger net health benefit. The likelihood of the current screening programme being the optimal scenario was less than 1%. No screening amongst the lowest risk group, and triannual, biennial and annual screening amongst the three higher risk groups was the optimal screening strategy from those evaluated. Conclusions We found that risk-stratified breast cancer screening has the potential to be beneficial for women at the population level, but the net health benefit will depend on the particular risk-based strategy

MesoGraph: Automatic profiling of mesothelioma subtypes from histological images.

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score