Body shape phenotypes of multiple anthropometric traits and cancer risk: a multi-national cohort study

Background - Classical anthropometric traits may fail to fully represent the relationship of weight, adiposity, and height with cancer risk. We investigated the associations of body shape phenotypes with the risk of overall and site-specific cancers. Methods - We derived four distinct body shape phenotypes from principal component (PC) analysis on height, weight, body mass index (BMI), waist (WC) and hip circumferences (HC), and waist-to-hip ratio (WHR). The study included 340,152 men and women from 9 European countries, aged mostly 35–65 years at recruitment (1990–2000) in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Cox proportional hazards regression was used to estimate multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Results - After a median follow-up of 15.3 years, 47,110 incident cancer cases were recorded. PC1 (overall adiposity) was positively associated with the risk of overall cancer, with a HR per 1 standard deviation (SD) increment equal to 1.07 (95% confidence interval 1.05 to 1.08). Positive associations were observed with 10 cancer types, with HRs (per 1 SD) ranging from 1.36 (1.30–1.42) for endometrial cancer to 1.08 (1.03–1.13) for rectal cancer. PC2 (tall stature with low WHR) was positively associated with the risk of overall cancer (1.03; 1.02–1.04) and five cancer types which were not associated with PC1. PC3 (tall stature with high WHR) was positively associated with the risk of overall cancer (1.04; 1.03–1.05) and 12 cancer types. PC4 (high BMI and weight with low WC and HC) was not associated with overall risk of cancer (1.00; 0.99–1.01). Conclusions - In this multi-national study, distinct body shape phenotypes were positively associated with the incidence of 17 different cancers and overall cancer

A reporting and analysis framework for structured evaluation of COVID-19 clinical and imaging data

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint Lesion(TM) software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany