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Abstract Purpose Tomosynthesis is proposed to improve breast cancer assessment and staging. We compared tomosynthesis and mammography in estimating the size of newly-diagnosed breast cancers. Methods All pathologically-confirmed cancers detected in the STORM-2 trial (90 cancers, 85 women) were retrospectively measured on tomosynthesis by two independent readers. One reader also measured cancers on mammography. Relative mean differences (MDs) and 95% limits of agreement (LOA) with pathology were estimated for tomosynthesis and mammography within a single reader (Analysis 1) and between two readers (Analysis 2). Results Where cancers were detected and hence measured by both tests, tomosynthesis overestimated pathologic size relative to mammography (Analysis 1: MD 5% versus 1%, Analysis 2: 7% versus 3%; P = 0.10 both analyses). There was similar, large measurement variability for both tests (LOA range: −60% to +166%). Overestimation by tomosynthesis was attributable to the subgroup with dense breasts (MDs = 12–13% versus 4% for mammography). There was low average bias for both tests in the low-density subgroup (MDs = 0–4%). LOA were larger in dense breasts for both tomosynthesis and mammography (P ≤ 0.02 all comparisons). Cancers detected only by tomosynthesis were more frequently in dense breasts (60–68%): for those tumours size was estimated with increased measurement variability (LOA ranging from −75% to +293%). Conclusions On average, tomosynthesis overestimates pathologic tumour size in women with dense breasts; that difference is more likely to impact management in women with larger tumours. The main advantage of tomosynthesis appears to be detecting mammographically-occult cancers; however tomosynthesis less accurately measured those cancers in dense breasts (large measurement variability). Highlights • Tumour size was overestimated by tomosynthesis relative to mammography. • Overestimation was attributable to the subgroup with high breast density. • There was low average bias for both tests in the low-density subgroup. • Cancers detected only by tomosynthesis were measured with large variability. [ABSTRACT FROM AUTHOR]

Introduction Current evidence for oncoplastic breast conservation (OBC) is based on single institutional series. Therefore, we carried out a population-based audit of OBC practice and outcomes in Scotland. Methods A predefined database of patients treated with OBC was completed retrospectively in all breast units practicing OBC in Scotland. Results 589 patients were included from 11 units. Patients were diagnosed between September 2005 and March 2017. High volume units performed a mean of 19.3 OBCs per year vs. low volume units who did 11.1 (p = 0.012). 23 different surgical techniques were used. High volume units offered a wider range of techniques (8–14) than low volume units (3–6) (p = 0.004). OBC was carried out as a joint operation involving a breast and a plastic surgeon in 389 patients. Immediate contralateral symmetrisation rate was significantly higher when OBC was performed as a joint operation (70.7% vs. not joint operations: 29.8%; p < 0.001). The incomplete excision rate was 10.4% and was significantly higher after surgery for invasive lobular carcinoma (18.9%; p = 0.0292), but was significantly lower after neoadjuvant chemotherapy (3%; p = 0.031). 9.2% of patients developed major complications requiring hospital admission. Overall the complication rate was significantly lower after neoadjuvant chemotherapy (p = 0.035). The 5 year local recurrence rate was 2.7%, which was higher after OBC for DCIS (8.3%) than invasive ductal cancer (1.6%; p = 0.026). 5-year disease-free survival was 91.7%, overall survival was 93.8%, and cancer-specific survival was 96.1%. Conclusion This study demonstrated that measured outcomes of OBC in a population-based multi-centre setting can be comparable to the outcomes of large volume single centre series. [ABSTRACT FROM AUTHOR]

Background & methods The prospective ‘ s creening with t omosynthesis or standard m ammography’ (STORM) trial recruited women participating in biennial breast screening in Italy (2011–2012), and compared sequential screen-readings based on 2D-mammography alone or based on tomosynthesis (integrated 2D/3D-mammography). The STORM trial showed that tomosynthesis screen-reading significantly increased breast cancer detection compared to 2D-mammography alone. The present study completes reporting of the trial by examining interval breast cancers ascertained at two year follow-up. Results 9 interval breast cancers were identified; the estimated interval cancer rate was 1.23/1000 screens [9/7292] (95%CI 0.56 to 2.34) or 1.24/1000 negative screens [9/7235] (95%CI 0.57 to 2.36). In concurrently screened women who attended the same screening services and received 2D-mammography, interval cancer rate was 1.60/1000 screens [40/25,058] (95% CI 1.14 to 2.17) or 1.61/1000 negative screens [40/24,922] (95% CI 1.15 to 2.18). Estimated screening sensitivity for the STORM trial was 85.5% [59/69] (95%CI 75.0%–92.8%), and that for 2D-mammography screening was 77.3% [136/176] (95%CI 70.4%–83.2%). Conclusion Interval breast cancer rate amongst screening participants in the STORM trial was marginally lower (and screening sensitivity higher) than estimates amongst 2D-screened women; these findings should be interpreted with caution given the small number of interval cases and the sample size of the trial. Much larger screening studies, or pooled analyses, are required to examine interval cancer rates arising after breast tomosynthesis screening versus digital mammography screening. [ABSTRACT FROM AUTHOR]

This paper will present findings from a longitudinal case study of the reconstruction of horizontal infrastructure networks in Christchurch, New Zealand, following the major earthquakes of 2010-2011. This involved exploring the role of governance in recovery and in particular, how funding mechanisms shape decisions for managing disaster risk. National policy on the funding of recovery that was geared towards direct replacement of existing infrastructure had a pivotal role in influencing design standards for infrastructure reconstruction. An outcome of this national policy, combined with constrained local financial resources, is that it was difficult to maximise the opportunity presented by a disaster to resolve the shortcomings of the existing infrastructure systems. This raises critical questions: whether it is appropriate to attempt to improve infrastructure in recovery and how local governments (or asset owners in general) may be better incentivised to invest proactively to reduce future disaster risk. Special (yet limited) betterment funds were made available for the Christchurch reconstruction and similarly for post-flood reconstruction that was in progress at the same time in Queensland, Australia (which will also be explored in the paper). However, recent recovery funding reviews conducted in New Zealand and Australia have identified a need to investigate incentives to increase proactive investment to limit damage. This paper explores this proposition and presents some of the challenges faced with respect to building back better in Christchurch’s reconstruction and the subsequent lessons for developing future funding mechanisms for disaster recovery. It demonstrates the need for clearer terms of engagement between central government and local government, and how insurance mechanisms could play a more effective role. [ABSTRACT FROM AUTHOR]

Background: There is substantial variability in the prognosis of acute low back pain (LBP). The ability to identify the probability of individual patients recovering by key time points would be valuable in making informed decisions about the amount and type of treatment to provide. Predicting recovery based on presentation 1-week after initially seeking care is clinically important and may be more accurate than predictions made at initial presentation. The aim of this study was to predict the probability of recovery at 1-week, 1-month and 3-months after 1-week review in patients who still have LBP 1-week after initially seeking care.Methods: The study sample comprised 1070 patients with acute LBP, with a pain score of ≥2 1-week after initially seeking care. The primary outcome measure was days to recovery from pain. Ten potential prognostic factors were considered for inclusion in a multivariable Cox regression model.Results: The final model included duration of current episode, number of previous episodes, depressive symptoms, intensity of pain at 1-week, and change in pain over the first week after seeking care. Depending on values of the predictor variables, the probability of recovery at 1-week, 1-month and 3-months after 1-week review ranged from 4% to 59%, 19% to 91% and 30% to 97%, respectively. The model had good discrimination (C = 0.758) and calibration.Conclusions: This study found that a model based on five easily collected variables could predict the probability of recovery at key time points in people who still have LBP 1-week after seeking care.Significance: A clinical prediction model based on five easily collected variables was able to predict the likelihood of recovery from an episode of acute LBP at three key time points. The model had good discrimination (C = 0.758) and calibration. [ABSTRACT FROM AUTHOR]

The treatments under comparison in a randomised trial should ideally have equal value and acceptability - a position of equipoise - to study participants. However, it is unlikely that true equipoise exists in practice, because at least some participants may have preferences for one treatment or the other, for a variety of reasons. These preferences may be related to study outcomes, and hence affect the estimation of the treatment effect. Furthermore, the effects of preferences can sometimes be substantial, and may even be larger than the direct effect of treatment. Preference effects are of interest in their own right, but they cannot be assessed in the standard parallel group design for a randomised trial. In this paper, we describe a model to represent the impact of preferences on trial outcomes, in addition to the usual treatment effect. In particular, we describe how outcomes might differ between participants who would choose one treatment or the other, if they were free to do so. Additionally, we investigate the difference in outcomes depending on whether or not a participant receives his or her preferred treatment, which we characterise through a so-called preference effect. We then discuss several study designs that have been proposed to measure and exploit data on preferences, and which constitute alternatives to the conventional parallel group design. Based on the model framework, we determine which of the various preference effects can or cannot be estimated with each design. We also illustrate these ideas with some examples of preference designs from the literature. [ABSTRACT FROM AUTHOR]