Background: Goblet cell adenocarcinoma (GCA) of the appendix is a rare and aggressive tumour with varying nomenclature and classification systems. This has led to heterogeneity in published data, and there is a lack of consensus on incidence, survival, and management.Methods: We provide an overview of GCA with a comprehensive systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and a retrospective analysis of all cases recorded in the English National Cancer Registration and Analysis Service database between 1995 and 2018. The Kaplan–Meier estimator was used to calculate overall survival, and Cox proportional hazards regression was used to identify prognostic factors.Results: The systematic review demonstrated an incidence of 0.05–0.3 per 100,000 per year among North American registry studies. The 1-, 3-, and 5-year survival rate was 95.5%, 85.9%–87.6%, and 76.0%–80.6%, respectively. Age, stage, and grade were identified as prognostic factors for survival. Our analysis included 1,225 cases. Age-standardised incidence was 0.0335 per year in 1995 and gradually rose to 0.158 per year in 2018. The 1-, 3-, and 5-year survival rate was 90.0% [95% confidence interval (95% CI): 85.4–94.0], 76.0% (95% CI: 73.8–80.9), and 68.6% (95% CI: 65.9–72.2), respectively. On univariate Cox regression analyses, female sex, stage, and grade were associated with worse overall survival. On multivariate analysis, only stage remained a statistically significant prognostic factor.Conclusions: GCA of the appendix is rare, but incidence is increasing. We report a lower incidence and survival than North American registry studies. Higher stage was associated with decreased survival. Further prospective studies are required to establish optimal management.

Assessment in African populations suggest adjustment for ethnicity in estimated glomerular filtration rate (eGFR) equations derived from African Americans lead to overestimation of GFR and failure to determine severity in chronic kidney disease (CKD). However, studies in African Europeans are limited. We aimed to assess accuracy of eGFR equations, with and without ethnicity adjustment compared with measured GFR in people of Black ethnicity in the United Kingdom. Performance of MDRD, CKD-EPI (with and without ethnicity adjustment), Full Age Spectrum (FAS), revised Lund Malmö (LM Revised), and European Kidney Function Consortium (EKFC) eGFR equations were assessed compared to ⁵¹Cr-EDTA GFR studies extracted from hospital databases. Participants with albumin <30g/l, liver disease, <18 years, of non-Black or non-White self-reported ethnicity were excluded. Agreement was assessed by bias, precision and 30%-accuracy and was stratified for ethnicity and GFR. 1888 ⁵¹Cr-EDTA studies were included (Mean age-53.7yrs; 43.6% female; 14.1% Black ethnicity). Compared to White participants, eGFR-MDRD and eGFR-CKD-EPI equations in Black participants significantly overestimated GFR (bias 20.3 and 19.7 ml/min/1.73m² respectively, p<0.001). Disregarding the ethnicity adjustment significantly improved GFR estimates for Black participants (bias 6.7 and 2.4ml/min/1.73m² for eGFR-MDRD and eGFR-CKD-EPI respectively, p<0.001). The LM Revised equation had the smallest bias for both White and Black participants (5.8ml and -1.1ml/min/1.73m² respectively). 30%-accuracy was superior for GFR≥60ml/min/1.73m² compared to <60ml/min/1.73m² using eGFR-CKD-EPI equation for both White and Black participants (p<0.001). Multivariate regression methodology with adjustment for age, sex and log(serum creatinine) in the cohort yielded an ethnicity coefficient of 1.018 (95% CI: 1.009–1.027). Overestimation of measured GFR with eGFR equations using ethnicity adjustment factors may lead to reduced CKD diagnosis and under-recognition of severity in people of Black ethnicity. Our findings suggest that ethnicity adjustment for GFR estimation in non-African Americans may not be appropriate for use in people of Black ethnicity in the UK.

We report a case of intravascular hemolysis and methemoglobinemia, precipitated by severe acute respiratory syndrome coronavirus 2 infection, in a patient with undiagnosed glucose-6-phosphate dehydrogenase deficiency. Clinicians should be aware of this complication of coronavirus disease as a cause of error in pulse oximetry and a potential risk for drug-induced hemolysis.

An amendment to this paper has been published and can be accessed via the original article.

Very few studies investigating COVID-19 in cancer patients have included cancer patients as controls. We aimed to identify factors associated with the risk of testing positive for SARS CoV2 infection in a cohort of cancer patients. We analyzed data from all cancer patients swabbed for COVID-19 between 1^st March and 31^st July 2020 at Guy’s Cancer Centre. We conducted logistic regression analyses to identify which factors were associated with a positive COVID-19 test. Results: Of the 2152 patients tested for COVID-19, 190 (9%) tested positive. Male sex, black ethnicity, and hematological cancer type were positively associated with risk of COVID-19 (OR = 1.85, 95%CI:1.37–2.51; OR = 1.93, 95%CI:1.31–2.84; OR = 2.29, 95%CI:1.45–3.62, respectively) as compared to females, white ethnicity, or solid cancer type, respectively. Male, Asian ethnicity, and hematological cancer type were associated with an increased risk of severe COVID-19 (OR = 3.12, 95%CI:1.58–6.14; OR = 2.97, 95%CI:1.00–8.93; OR = 2.43, 95%CI:1.00–5.90, respectively). This study is one of the first to compare the risk of COVID-19 incidence and severity in cancer patients when including cancer patients as controls. Results from this study have echoed those of previous reports, that patients who are male, of black or Asian ethnicity, or with a hematological malignancy are at an increased risk of COVID-19.

Globally, lung cancer remains the most common cause of cancer mortality, with non-small-cell lung cancer (NSCLC) being the most common subtype of lung cancer diagnosed. This review paper provides a comprehensive landscape of clinical prediction models (CPMs) in NSCLC, including in early-stage and metastatic disease, and the recent acceleration of artificial intelligence integration. Prediction models are developed using multimodal patient data to allow oncologists to make evidence-based decisions regarding patient treatment options. Despite these models in early-stage and metastatic NSCLC showing promise, their clinical application provides challenges, involving an unmet need for external validation, alongside a lack of prospective modelling. However, the continued advancements in this field, comprising production and accessibility of large-scale pathology databases and external validation of developed models, allow for continued research and progress. These models have potential to assist in personalised treatment selection, supporting oncologists in perceiving future risk factors or issues associated with a specific targeted therapy for an individual patient, ultimately optimising treatment to precise, personalised options for individuals diagnosed with NSCLC.