Impact of Screening on Breast Cancer Mortality: The UK Program 20 Years On

This study was funded by a grant from the UK Department of Health (no. 106/0001). The grant was awarded to Prof Stephen W Duffy

An ongoing case-control study to evaluate the NHS Bowel Cancer Screening Programme

© 2014 Massat et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated

Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case-control study.

Funder: Policy Research Unit in Cancer Awareness, Screening and early Diagnosis, PR-PRU-1217-21601Funder: American Cancer Society NHPDCSGBR-GBRLONG Policy Research Unit in Cancer Awareness, Screening and early Diagnosis, PR-PRU-1217-21601BACKGROUND: This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers. METHODS: This case-control study of 1204 women aged 47-73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls. RESULTS: FGV, VBD, VAS, and DG all discriminated interval cancers (all p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD (0.63 and 0.53, respectively, p < 0.001). CONCLUSION: FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk

A case–control study to evaluate the impact of the breast screening programme on breast cancer incidence in England

Abstract Background There is uncertainty about overdiagnosis in mammography screening. Methods We aimed to estimate the effect of screening on breast cancer incidence and overdiagnosis in the NHS Breast Screening Programme in England. The study included 57,493 cases and 105,653 controls, with cases defined as women diagnosed at ages 47–89 with primary breast cancer, invasive or ductal carcinoma in situ, in 2010 or 2011. Where possible, two controls were selected per case, matched on date of birth and screening area. Conditional logistic regression was used to estimate the effect of screening on breast cancer risk, with adjustment for potential self‐selection bias. Results were combined with national incidence data to estimate absolute rates of overdiagnosis. Overdiagnosis was calculated as the cumulative excess of cancers diagnosed in the age group 50–77 in a woman attending three‐yearly screening between ages 50 and 70 compared with a woman attending no screens. Results The estimated number of cases overdiagnosed in women attending all screens in the programme was 679.3 per 100,000 without adjustment for self‐selection bias and 261.2 per 100,000 with adjustment. These corresponded to an estimated 9.5% of screen‐detected cancers overdiagnosed without adjustment and 3.7% with adjustment for self‐selection. Conclusions The NHS Breast Screening Programme in England confers at worst modest levels of overdiagnosis

Effects of agricultural production systems and their components on protein profiles of potato tubers

A range of studies have compared the level of nutritionally relevant compounds in crops from organic and nonorganic farming systems, but there is very limited information on the effect of farming systems and their key components on the protein composition of plants. We addressed this gap by quantifying the effects of different farming systems and key components of such systems on the protein profiles of potato tubers. Tuber samples were produced in the Nafferton factorial systems study, a group of long-term, replicated factorial field experiments designed to identify and quantify the effect of fertility management methods, crop protection practices and rotational designs used in organic, low input and conventional production systems. Protein profiles were determined by 2-DE and subsequent protein identification by HPLC-ESI-MS/MS. Principal component analysis of 2-DE data showed that only fertility management practices (organic matter vs. mineral fertiliser based), had a significant effect on protein composition. Quantitative differences were detected in 160 of the 1100 tuber proteins separated by 2-DE. Proteins identified by MS are involved in protein synthesis and turnover, carbon and energy metabolism and defence responses, suggesting that organic fertilisation leads to an increased stress response in potato tubers