Background Conflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity. Methods We undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase). Results There were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1–9/day: OR 2.14, 95% CI 0.87 to 5.24; 10–19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72). Interpretation Congruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.

The traditionally promulgated perspectives of neuroendocrine neoplasms (NEN) as rare, indolent tumours are blunt and have been outdated for the last 2 decades. Clear increments in their incidence over the past decades render them increasingly clinically relevant, and at initial diagnosis many present with nodal and/or distant metastases (notably hepatic). The molecular pathogenesis of these tumours is increasingly yet incompletely understood. Those arising from the small bowel (SB) or pancreas typically occur sporadically; the latter may occur within the context of hereditary tumour predisposition syndromes. NENs can also be associated with endocrinopathy of hormonal hypersecretion. Tangible advances in the development of novel biomarkers, functional imaging modalities and therapy are especially applicable to this sub-set of tumours. The management of SB and pancreatic neuroendocrine tumours (NET) may be challenging, and often comprises a multidisciplinary approach wherein surgical, medical, interventional radiological and radiotherapeutic modalities are implemented. This review provides a comprehensive overview of the epidemiology, pathophysiology, diagnosis and treatment of SB and pancreatic NETs. Moreover, we provide an outlook of the future in these tumour types which will include the development of precision oncology frameworks for individualised therapy, multi-analyte predictive biomarkers, artificial intelligence-derived clinical decision support tools and elucidation of the role of the microbiome in NEN development and clinical behaviour.

Objective To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches. Design Population based cohort study. Setting QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers. Participants 141 765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020. Main outcome measures Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility. Results During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21 688 breast cancer related deaths and 11 454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20 367 breast cancer related deaths occurred during a total of 688 564.81 person years. The crude breast cancer mortality rate was 295.79 per 10 000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox model’s random effects meta-analysis pooled estimate for Harrell’s C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrell’s C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrell’s C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches. Conclusion In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up.

Background: Appendiceal neuroendocrine neoplasms (ANEN) are mostly indolent tumours treated effectively with simple appendectomy. However, controversy exists regarding the necessity of oncologic right hemicolectomy (RH) in patients with histologic features suggestive of more aggressive disease. We assess the effects of current guidelines in selecting the surgical strategy (appendectomy or RH) for the management of ANEN. Methods/Aims: This is a retrospective review of all ANEN cases treated over a 14-year period at 3 referral centres and their management according to consensus guidelines of the European and the North American Neuroendocrine Tumor Societies (ENETS and NANETS, respectively). The operation performed, the tumour stage and grade, the extent of residual disease, and the follow-up outcomes were evaluated. Results: Of 14,850 patients who had appendectomies, 215 (1.45%) had histologically confirmed ANEN. Four patients had synchronous non-ANEN malignancies. One hundred and ninety-three patients had index appendectomy. Seventeen patients (7.9%) had lymph node metastases within the mesoappendix. Forty-nine patients underwent RH after appendectomy. The percentages of 30-day morbidity and mortality after RH were 2 and 0%, respectively. Twelve patients (24.5%) receiving completion RH were found to have lymph node metastases. Two patients had liver metastases, both of them synchronous. The median follow-up was 38.5 months (range 1-143). No patient developed disease recurrence. Five- and 10-year overall survival for all patients with ANEN as the only malignancy was both 99.05%. Conclusions: The current guidelines appear effective in identifying ANEN patients at risk of harbouring nodal disease, but they question the oncological relevance of ANEN lymph node metastases. RH might present an overtreatment for a number of patients with ANEN.

This review evaluates the diagnostic efficacy of different morphological and functional imaging modalities (ultrasound [US], computed tomography [CT], magnetic resonance [MR] imaging, scintigraphy, and positron emission tomography [PET]) in detecting neuroendocrine liver metastases (NELM), assessing vascular and biliary involvement, and the presence of extrahepatic disease. MR imaging is superior for depicting NELM compared to US, CT, and somatostatin receptor scintigraphy. Diffusion-weighted MR imaging is more sensitive for detecting NELM than both T2-weighted and dynamic gadolinium-enhanced MR sequences, and should be systematically performed. Similarly, gadoxetic acid-enhanced MR imaging is more sensitive for detecting liver metastases than conventional extracellular gadolinium chelate-enhanced MR sequences. Its role in detecting NELM remains investigational but appears promising. Somatostatin receptor-targeted PET/CT is a highly effective approach in assessing the resectability of well-differentiated NELM due to very high specificity (and high sensitivity) and its ability to detect small volume extrahepatic disease; this molecular imaging modality is becoming increasingly available in and outside Europe after the recent approval of ⁶⁸68-DOTATATE in the US. In addition, the information from multiphase, contrast-enhanced CT with 3D reconstruction - obtained concurrently with the information on somatostatin receptor expression of the metastases - is very helpful in planning the extent and type of resection of NELM.