Efficacy of extended adjuvant therapy with aromatase inhibitors in early breast cancer among common clinicopathologically-defined subgroups: A systematic review and meta-analysis

BackgroundRandomized controlled trials (RCTs) have shown improvements in breast cancer outcomes from extending treatment with aromatase inhibitors (AIs) beyond the initial 5 years after diagnosis. Consistency of this effect in common clinicopathologically defined subgroups was not been reported systematically.MethodsWe identified RCTs comparing extended AIs to placebo or no treatment using a systematic search of MEDLINE and a review of abstracts from key conferences between 2013 and 2016. Hazard ratios (HRs) and 95% confidence intervals (CI) for disease-free survival (DFS) were included in a meta-analysis using generic inverse variance and random effects modelling. Pre-specified subgroups included: age (<60 ± 5 years versus ≥60 ± 5 years), tumor size (>2 cm versus ≤2 cm), nodal status (positive versus negative), hormone receptor status (double versus single receptor expression) and receipt of adjuvant chemotherapy (yes versus no).ResultsSeven trials comprising 16,349 patients were analyzed. Overall, the effect of extended AIs was similar in all subgroups. Non-significantly greater effect sizes were seen in patients with larger tumors (HR for DFS 0.77 versus 0.88, p for difference = 0.44), nodal involvement (HR = 0.72 versus 0.83, p for difference = 0.22), double hormone receptor expression (HR = 0.68 versus 1.01, p for difference = 0.31) and receipt of adjuvant chemotherapy (HR = 0.71 versus 0.80, p for difference = 0.51).ConclusionsExtended treatment with AIs is associated with similar relative improvements in DFS in all subgroups analyzed. The combination of greater effect size and higher absolute risk of recurrence in node positive and larger tumors will likely translate to higher absolute benefits from extended AIs in these groups.     

When Artificial Intelligence Models Surpass Physician Performance: Medical Malpractice Liability in an Era of Advanced Artificial Intelligence

It seems inevitable that diagnostic and recommender artificial intelligence models will ultimately reach a point when they outperform human clinicians. Just as antibiotics displaced a host of medicinals for treating infections, the superior performance of such models will force their adoption. This article contemplates certain ethical and legal implications bearing on that adoption, especially because they involve a clinician’s exposure to allegations of malpractice. The article discusses four relevant considerations: (1) the imperative of using explainable artificial intelligence models in clinical care, (2) specific strategies for diminishing liability when a clinician agrees or disagrees with a model’s findings or recommendations but the patient nevertheless experiences a poor outcome, (3) relieving liability through legislation or regulation, and (4) comprehending such models as “persons” and therefore as potential defendants in legal proceedings. We conclude with observations on clinician–vendor relationships and argue that, although advanced artificial intelligence models have not yet arrived, clinicians must begin considering their implications now.     

Introduction and overview of the special issue “Brain imaging and aging”: The new era of neuroimaging in aging research

It is well known that the brain is one of the organs particularly affected by aging in terms of function, relative to the gastrointestinal tract and liver, which exhibit less functional decline. There is also a wide range of age-related neurological disorders such as stroke, Alzheimer’s disease, and Parkinson’s disease. Therefore, it is very important to understand the relationship between functional age-related change and neurological dysfunction. Neuroimaging techniques including magnetic resonance imaging and positron emission tomography have been significantly improved over recent years. Many physicians and researchers have investigated various mechanisms of age-related cerebral change and associated neurological disorders using neuroimaging techniques. In this special issue of Ageing Research Reviews, we focus on cerebral- and neuro-imaging, which are a range of tools used to visualize structure, functions, and pathogenic molecules in the nervous system. In addition, we summarize several review articles about the history, present values, and future perspectives of neuroimaging modalities.