Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients

© The Author(s) 2019. Published by Springer Nature on behalf of Cancer Research UK.BACKGROUND: An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK. METHODS: Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity. RESULTS: None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model. CONCLUSION: PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.Peer reviewe

Using mammographic density to predict breast cancer risk: dense area or percentage dense area.

INTRODUCTION: Mammographic density (MD) is one of the strongest risk factors for breast cancer. It is not clear whether this association is best expressed in terms of absolute dense area or percentage dense area (PDA). METHODS: We measured MD, including nondense area (here a surrogate for weight), in the mediolateral oblique (MLO) mammogram using a computer-assisted thresholding technique for 634 cases and 1,880 age-matched controls from the Cambridge and Norwich Breast Screening programs. Conditional logistic regression was used to estimate the risk of breast cancer, and fits of the models were compared using likelihood ratio tests and the Bayesian information criteria (BIC). All P values were two-sided. RESULTS: Square-root dense area was the best single predictor (for example, χ₁² = 53.2 versus 44.4 for PDA). Addition of PDA and/or square-root nondense area did not improve the fit (both P > 0.3). Addition of nondense area improved the fit of the model with PDA (χ₁² = 11.6; P < 0.001). According to the BIC, the PDA and nondense area model did not provide a better fit than the dense area alone model. The fitted values of the two models were highly correlated (r = 0.97). When a measure of body size is included with PDA, the predicted risk is almost identical to that from fitting dense area alone. CONCLUSIONS: As a single parameter, dense area provides more information than PDA on breast cancer risk

Effects on Cancer Prevention from the COVID-19 Pandemic

The COVID-19 pandemic led to disruption of health services around the world, including cancer services. We carried out a narrative review of the effect of the pandemic on cancer prevention services, including screening. Services were severely affected in the early months of the pandemic, and in some areas are still recovering. Large numbers of additional cancers or additional late-stage cancers have been predicted to arise over the coming years as a result of this disruption. To minimize the effects on cancer outcomes, it is necessary to return as quickly as possible to prepandemic levels of screening and prevention activity or indeed to exceed these levels. The recovery of services should address health inequalities.</p

Overdiagnosis in breast cancer screening: the importance of length of observation period and lead time

PMCID: PMC3706885This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Effects on Cancer Prevention from the COVID-19 Pandemic

The COVID-19 pandemic led to disruption of health services around the world, including cancer services. We carried out a narrative review of the effect of the pandemic on cancer prevention services, including screening. Services were severely affected in the early months of the pandemic, and in some areas are still recovering. Large numbers of additional cancers or additional late-stage cancers have been predicted to arise over the coming years as a result of this disruption. To minimize the effects on cancer outcomes, it is necessary to return as quickly as possible to prepandemic levels of screening and prevention activity or indeed to exceed these levels. The recovery of services should address health inequalities.</p

Mammographic breast density: comparison of methods for quantitative evaluation.

PURPOSE: To evaluate the results from two software tools for measurement of mammographic breast density and compare them with observer-based scores in a large cohort of women. MATERIALS AND METHODS: Following written informed consent, a data set of 36 281 mammograms from 8867 women were collected from six United Kingdom centers in an ethically approved trial. Breast density was assessed by one of 26 readers on a visual analog scale and with two automated density tools. Mean differences were calculated as the mean of all the individual percentage differences between each measurement for each case (woman). Agreement in total breast volume, fibroglandular volume, and percentage density was assessed with the Bland-Altman method. Association with observer's scores was calculated by using the Pearson correlation coefficient (r). RESULTS: Correlation between the Quantra and Volpara outputs for total breast volume was r = 0.97 (P < .001), with a mean difference of 43.5 cm(3) for all cases representing 5.0% of the mean total breast volume. Correlation of the two measures was lower for fibroglandular volume (r = 0.86, P < .001). The mean difference was 30.3 cm(3) for all cases representing 21.2% of the mean fibroglandular tissue volume result. Quantra gave the larger value and the difference tended to increase with volume. For the two measures of percentage volume density, the mean difference was 1.61 percentage points (r = 0.78, P < .001). Comparison of observer's scores with the area-based density given by Quantra yielded a low correlation (r = 0.55, P < .001). Correlations of observer's scores with the volumetric density results gave r values of 0.60 (P < .001) and 0.63 (P < .001) for Quantra and Volpara, respectively. CONCLUSION: Automated techniques for measuring breast density show good correlation, but these are poorly correlated with observer's scores. However automated techniques do give different results that should be considered when informing patient personalized imaging. (©) RSNA, 2015 Clinical trial registration no. ISRCTN 73467396.Supported by the National Institute for Health Research’s Health Technology Assessment Programme.This is the final version of the article. It first appeared at http://pubs.rsna.org/doi/full/10.1148/radiol.1414150

Vacancies, disorder-induced smearing of the electronic structure, and its implications for the superconductivity of anti-perovskite MgC0.93_{0.93}Ni2.85_{2.85}

The anti-perovskite superconductor MgC$_{0.93}$Ni$_{2.85}$ was studied using high-resolution x-ray Compton scattering combined with electronic structure calculations. Compton scattering measurements were used to determine experimentally a Fermi surface that showed good agreement with that of our supercell calculations, establishing the presence of the predicted hole and electron Fermi surface sheets. Our calculations indicate that the Fermi surface is smeared by the disorder due to the presence of vacancies on the C and Ni sites, but does not drastically change shape. The 20\% reduction in the Fermi level density-of-states would lead to a significant ($\sim 70\%$) suppression of the superconducting $T_c$ for pair-forming electron-phonon coupling. However, we ascribe the observed much smaller $T_c$ reduction at our composition (compared to the stoichiometric compound) to the suppression of pair-breaking spin fluctuations.Comment: 11 pages, 3 figure

A method for exploratory repeated-measures analysis applied to a breast-cancer screening study

When a model may be fitted separately to each individual statistical unit, inspection of the point estimates may help the statistician to understand between-individual variability and to identify possible relationships. However, some information will be lost in such an approach because estimation uncertainty is disregarded. We present a comparative method for exploratory repeated-measures analysis to complement the point estimates that was motivated by and is demonstrated by analysis of data from the CADET II breast-cancer screening study. The approach helped to flag up some unusual reader behavior, to assess differences in performance, and to identify potential random-effects models for further analysis.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS481 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org