Adjuvant chemotherapy and survival among patients 70 years of age and younger with node-negative breast cancer and the 21-gene recurrence score of 26-30

BACKGROUND: The benefits of chemotherapy in node-negative, hormone receptor-positive, and human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients with the 21-gene recurrence score (RS) of 18-30, particularly those with RS 26-30, are not known. METHODS: Using the Surveillance, Epidemiology, and End Results (SEER) data, we retrospectively identified 29,137 breast cancer patients with the 21-gene RS of 18-30 diagnosed between 2004 and 2015. Mortality risks according to the RS and chemotherapy use were compared by the Kaplan-Meier method and Cox\u27s proportional hazards model. RESULTS: Among the breast cancer patients with the RS 18-30, 21% of them had RS 26-30. Compared to breast cancer patients with RS 18-25, patients with RS 26-30 had more aggressive tumor characteristics and chemotherapy use and increased risk of breast cancer-specific mortality and overall mortality. In breast cancer patients who were aged ≤ 70 years and had RS of 26-30, chemotherapy administration was associated with a 32% lower risk of breast cancer-specific mortality (hazard ratio [HR], 0.68; 95% confidence interval [CI], 0.47-0.99) and a 42% lower risk of overall mortality (HR, 0.58; 95% CI, 0.44-0.76). Survival benefits were most pronounced in breast cancer patients who were younger or had grade III tumor. CONCLUSIONS: The 21-gene RS of 18-30 showed heterogeneous outcomes, and the RS 26-30 was a significant prognostic factor for an increased risk of mortality. Adjuvant chemotherapy could improve the survival of node-negative, hormone receptor-positive, and HER2-negative breast cancer patients with the 21-gene RS 26-30 and should be considered for patients, especially younger patients or patients with high-grade tumors

Digital Museum and User Experience: The Case of Google Art & Culture

Museum websites have evolved from offering information on the collections of institutions over the virtual space to providing the richer user experience. However, previous re-search in museology has mainly focused on the causal rela-tionship between online users and actual visitors of physical museums, neglecting users’ behaviour within the digital platform or human-computer interaction (HCI). This study aims to explore the way in which online users are affected by the interface tools of digital museums with a case study of the Google Art & Culture. Drawing on the concept of re-mediation [1], our analysis reinforces the interactivity based on its interface tools such as “Zoom-in” and “Museum View” for delivering information (transparency) and “User Gal-lery”, “Share”, and “Details” for compelling experience (re-flectivity). The outcome of this research suggests ways in which museum professionals can develop and manage user interface of their institutions

Reflective Remediation as Critical Design Strategy: Lessons from László Moholy-Nagy and Olafur Eliasson

Reflective remediation is an important component of contemporary media theory, which emphasises the creative efforts of avant-garde artists and designers to shape the evolution of media in a critical way. However, the critical capacity of reflective remediations may be compromised by commercial dynamics or conventions, such as the celebration of ‘reflectivity for reflectivity’s sake’ that aims to construct an auratic experience for viewers. Because reflectivity is a critical media practice, it is vital to investigate reflective remediations in tandem with the critical intensions and creative visions of artists and designers. We investigate the critical media practices of the Bauhaus master, László Moholy-Nagy (1895-1946) who explored the concept of ‘productive creativity’, according to which creative experimentation should lead to design knowledge, redefining the relationship between what is known and unknown. We then scrutinise the artistic practice of the Icelandic-Danish contemporary artist Olafur Eliasson (b.1967), who contextualises reflectivity as an embodied experience, in terms of what he calls ‘frictional encounters’. When applied together, the two concepts enhance our understanding of reflective remediation as a critical design strategy

Prediagnosis Leisure-Time Physical Activity and Lung Cancer Survival: A Pooled Analysis of 11 Cohorts

Background: Little is known about the association between physical activity before cancer diagnosis and survival among lung cancer patients. In this pooled analysis of 11 prospective cohorts, we investigated associations of prediagnosis leisuretime physical activity (LTPA) with all-cause and lung cancer–specific mortality among incident lung cancer patients. Methods: Using self-reported data on regular engagement in exercise and sports activities collected at study enrollment, we assessed metabolic equivalent hours (MET-h) of prediagnosis LTPA per week. According to the Physical Activity Guidelines for Americans, prediagnosis LTPA was classified into inactivity, less than 8.3 and at least 8.3 MET-h per week (the minimum recommended range). Cox regression was used to estimate hazard ratios (HRs) and 95% confidence interval (CIs) for all-cause and lung cancer–specific mortality after adjustment for major prognostic factors and lifetime smoking history. Results: Of 20 494 incident lung cancer patients, 16 864 died, including 13 596 deaths from lung cancer (overall 5-year relative survival rate ¼ 20.9%, 95% CI ¼ 20.3% to 21.5%). Compared with inactivity, prediagnosis LTPA of more than 8.3 MET-h per week was associated with a lower hazard of all-cause mortality (multivariable-adjusted HR ¼ 0.93, 95% CI ¼ 0.88 to 0.99), but not with lung cancer–specific mortality (multivariable-adjusted HR ¼ 0.99, 95% CI ¼ 0.95 to 1.04), among the overall population. Additive interaction was found by tumor stage (Pinteraction ¼ .008 for all-cause mortality and .003 for lung cancer–specific mortality). When restricted to localized cancer, prediagnosis LTPA of at least 8.3 MET-h per week linked to 20% lower mortality: multivariableadjusted HRs were 0.80 (95% CI¼ 0.67 to 0.97) for all-cause mortality and 0.80 (95% CI¼ 0.65 to 0.99) for lung cancer–specific mortality. Conclusions: Regular participation in LTPA that met or exceeded the minimum Physical Activity Guidelines was associated with reduced hazards of mortality among lung cancer patients, especially those with early stage cancer

Association between Class III Obesity (BMI of 40–59 kg/m2) and Mortality: A Pooled Analysis of 20 Prospective Studies

Background: The prevalence of class III obesity (body mass index [BMI]≥40 kg/m2) has increased dramatically in several countries and currently affects 6% of adults in the US, with uncertain impact on the risks of illness and death. Using data from a large pooled study, we evaluated the risk of death, overall and due to a wide range of causes, and years of life expectancy lost associated with class III obesity. Methods and Findings: In a pooled analysis of 20 prospective studies from the United States, Sweden, and Australia, we estimated sex- and age-adjusted total and cause-specific mortality rates (deaths per 100,000 persons per year) and multivariable-adjusted hazard ratios for adults, aged 19–83 y at baseline, classified as obese class III (BMI 40.0–59.9 kg/m2) compared with those classified as normal weight (BMI 18.5–24.9 kg/m2). Participants reporting ever smoking cigarettes or a history of chronic disease (heart disease, cancer, stroke, or emphysema) on baseline questionnaires were excluded. Among 9,564 class III obesity participants, mortality rates were 856.0 in men and 663.0 in women during the study period (1976–2009). Among 304,011 normal-weight participants, rates were 346.7 and 280.5 in men and women, respectively. Deaths from heart disease contributed largely to the excess rates in the class III obesity group (rate differences = 238.9 and 132.8 in men and women, respectively), followed by deaths from cancer (rate differences = 36.7 and 62.3 in men and women, respectively) and diabetes (rate differences = 51.2 and 29.2 in men and women, respectively). Within the class III obesity range, multivariable-adjusted hazard ratios for total deaths and deaths due to heart disease, cancer, diabetes, nephritis/nephrotic syndrome/nephrosis, chronic lower respiratory disease, and influenza/pneumonia increased with increasing BMI. Compared with normal-weight BMI, a BMI of 40–44.9, 45–49.9, 50–54.9, and 55–59.9 kg/m2 was associated with an estimated 6.5 (95% CI: 5.7–7.3), 8.9 (95% CI: 7.4–10.4), 9.8 (95% CI: 7.4–12.2), and 13.7 (95% CI: 10.5–16.9) y of life lost. A limitation was that BMI was mainly ascertained by self-report. Conclusions: Class III obesity is associated with substantially elevated rates of total mortality, with most of the excess deaths due to heart disease, cancer, and diabetes, and major reductions in life expectancy compared with normal weight. Please see later in the article for the Editors' Summar

Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI

Retrospective evaluation of data was approved by the local ethics committee and informed consent was waived. A total of 143 patients (two independent cohorts for discovery [n = 86; glioblastoma = 49, PCNSL = 37] and validation [n = 57; glioblastoma = 29, PCNSL = 28]) with newly diagnosed glioblastoma and PCNSL were subjected to radiomics analysis using the multi-parametric MRI (contrast-enhanced T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging). Radiomics analyses were performed for two types of regions of interest (ROI) covering contrast-enhancing tumor and whole (enhancing or non-enhancing) tumor plus peritumoral edema. A total of 127 radiomics features were calculated. Feature selection was performed to identify the most discriminating features for every MR image in the discovery cohort. The identified features were used to calculate radiomics scores, which were later used in logistic regression to distinguish between PCNSL and glioblastoma. The classification model was further tested on the independent validation cohort. Results Fifteen features were selected as significant features in the discovery cohort. Using the identified features and calculated radiomics scores, the logistic regression-based classifier yielded an area under the curve (AUC) of 0.979, sensitivity of 0.938, and specificity of 0.944 in the discovery cohort to distinguish between glioblastoma and PCNSL. A similarly high rate of performance was observed in the validation cohort (AUC = 0.956) (c) Springer-Verlag GmbH Germany, part of Springer Nature 201