RE: Weight gain after breast cancer diagnosis and all-cause mortality: Systematic review and meta-Analysis

In the meta-analysis by Playdon et al. (1) in the Journal, the authors conclude

Prediagnosis social support, social integration, living status, and colorectal cancer mortality in postmenopausal women from the women's health initiative

Background: We evaluated associations between perceived social support, social integration, living alone, and colorectal cancer (CRC) outcomes in postmenopausal women. Methods: The study included 1431 women from the Women's Health Initiative who were diagnosed from 1993 through 2017 with stage I through IV CRC and who responded to the Medical Outcomes Study Social Support survey before their CRC diagnosis. We used proportional hazards regression to evaluate associations of social support (tertiles) and types of support, assessed up to 6 years before diagnosis, with overall and CRC-specific mortality. We also assessed associations of social integration and living alone with outcomes also in a subset of 1141 women who had information available on social ties (marital/partner status, community and religious participation) and living situation. Results: In multivariable analyses, women with low (hazard ratio [HR], 1.52; 95% CI, 1.23-1.88) and moderate (HR, 1.21; 95% CI, 0.98-1.50) perceived social support had significantly higher overall mortality than those with high support (P [continuous] <.001). Similarly, women with low (HR, 1.42; 95% CI, 1.07-1.88) and moderate (HR, 1.28; 95% CI, 0.96-1.70) perceived social support had higher CRC mortality than those with high social support (P [continuous] =.007). Emotional, informational, and tangible support and positive interaction were all significantly associated with outcomes, whereas affection was not. In main-effects analyses, the level of social integration was related to overall mortality (P for trend =.02), but not CRC mortality (P for trend =.25), and living alone was not associated with mortality outcomes. However, both the level of social integration and living alone were related to outcomes in patients with rectal cancer. Conclusions: Women with low perceived social support before diagnosis have higher overall and CRC-specific mortality

Impact of Structural Cerebral Damage in Adults With Tetralogy of Fallot

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Longitudinal relation between blood pressure, antihypertensive use and cerebral blood flow, using arterial spin labelling MRI

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The spatial coefficient of variation in arterial spin labeling cerebral blood flow images

Item does not contain fulltextMacro-vascular artifacts are a common arterial spin labeling (ASL) finding in populations with prolonged arterial transit time (ATT) and result in vascular regions with spuriously increased cerebral blood flow (CBF) and tissue regions with spuriously decreased CBF. This study investigates whether there is an association between the spatial signal distribution of a single post-label delay ASL CBF image and ATT. In 186 elderly with hypertension (46% male, 77.4 +/- 2.5 years), we evaluated associations between the spatial coefficient of variation (CoV) of a CBF image and ATT. The spatial CoV and ATT metrics were subsequently evaluated with respect to their associations with age and sex - two demographics known to influence perfusion. Bland-Altman plots showed that spatial CoV predicted ATT with a maximum relative error of 7.6%. Spatial CoV was associated with age (beta = 0.163, p = 0.028) and sex (beta = -0.204, p = 0.004). The spatial distribution of the ASL signal on a standard CBF image can be used to infer between-participant ATT differences. In the absence of ATT mapping, the spatial CoV may be useful for the clinical interpretation of ASL in patients with cerebrovascular pathology that leads to prolonged transit of the ASL signal to tissue

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks

BACKGROUND AND PURPOSE: Supervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require manual lesion annotations for model development, while unsupervised deep learning methods such as generative adversarial networks do not. The aim of this study was to develop and evaluate a generative adversarial network to segment infarct and hemorrhagic stroke lesions on follow-up NCCT scans. MATERIALS AND METHODS: Training data consisted of 820 patients with baseline and follow-up NCCT from 3 Dutch acute ischemic stroke trials. A generative adversarial network was optimized to transform a follow-up scan with a lesion to a generated baseline scan without a lesion by generating a difference map that was subtracted from the follow-up scan. The generated difference map was used to automatically extract lesion segmentations. Segmentation of primary hemorrhagic lesions, hemorrhagic transformation of ischemic stroke, and 24-hour and 1-week follow-up infarct lesions were evaluated relative to expert annotations with the Dice similarity coefficient, Bland-Altman analysis, and intraclass correlation coefficient. RESULTS: The median Dice similarity coefficient was 0.31 (interquartile range, 0.08-0.59) and 0.59 (interquartile range, 0.29-0.74) for the 24-hour and 1-week infarct lesions, respectively. A much lower Dice similarity coefficient was measured for hemorrhagic transformation (median, 0.02; interquartile range, 0-0.14) and primary hemorrhage lesions (median, 0.08; interquartile range, 0.01-0.35). Predicted lesion volume and the intraclass correlation coefficient were good for the 24-hour (bias, 3 mL; limits of agreement, -64-59mL; intraclass correlation coefficient, 0.83; 95% CI, 0.78-0.88) and excellent for the 1-week (bias, -4 m; limits of agreement,-66-58 mL; intraclass correlation coefficient, 0.90; 95% CI, 0.83-0.93) follow-up infarct lesions. CONCLUSIONS: An unsupervised generative adversarial network can be used to obtain automated infarct lesion segmentations with a moderate Dice similarity coefficient and good volumetric correspondence