BackgroundTo determine, in a meta-analysis, the diagnostic performance of quantitative diffusion-weighted (DW) MR imaging in patients with breast lesions.MethodsEnglish and Chinese studies published prior to June 2009 to assess the diagnostic performance of quantitative DWI in patients with breast lesions were reviewed and summarized with reference to the inclusion and exclusion criteria. Methodological quality was assessed by using the quality assessment of diagnostic studies (QUADAS) instrument. Publication bias analysis was performed by using Comprehensive Meta-analysis version 2. Meta-Disc version 1.4 was used to describe primary results and explore homogeneity by Chi-square test and inconsistency index; to explore threshold effect by receiver operator characteristic (ROC) space and Spearman correlation coefficient; and to pool weighted sensitivity and specificity by fixed or random effect model. A summary ROC (sROC) curve was constructed to calculate the area under the curve (AUC).ResultsOf 65 eligible studies, 13 with 615 malignant and 349 benign lesions were included in the original meta-analysis, among which heterogeneity arising from factors other than threshold effect and publication bias was explored. Methodological quality was moderate. The pooled weighted sensitivity and specificity with corresponding 95% confidence interval (CI) in one homogenous subgroup of studies using maximum b = 1000 s/mm2 were 0.84 (0.80, 0.87) and 0.84 (0.79, 0.88) respectively. AUC of sROC was 0.9085. Sensitivity analysis demonstrated that the pooled estimates were stable and reliable.ConclusionsQuantitative DWI has a higher specificity to differentiate between benign and malignant breast lesions compared to that of contrast-enhanced MRI. However, large scale randomized control trials (RCTs) are necessary to assess its clinical value because of disunified diffusion gradient factor b and diagnosis threshold.
Ueno and Ohata (1996) pointed out the importance of the correction of precipitation measurements on the Tibetan Plateau. The present author offers some comments to evaluate more quantitatively their results, which are summarized as follows: (1) the validity of the correction of precipitation should be checked for the individual cases, along with the total amount; (2) the diameter of the gauge should be investigated for any systematic bias of the measured precipitation; (3) the increment obtained through the correction procedure should be quantitatively compared with the standard error of the corresponding regression analysis; and (4) the effect of the correction should be looked at from various viewpoints, e. g., quantitative comparisons of the corrected precipitation with precipitation estimates from space, as well as with the surface energy budget on the Tibetan Plateau.
Alpine vegetation on the Tibetan Plateau (TP) is known to be sensitive to both climate change and anthropogenic disturbance. However, the magnitude and patterns of alpine vegetation dynamics and the driving mechanisms behind their variation on the TP remains under debate. In this study, we used updated MODIS Collection 6 Normalized Difference Vegetation Index (NDVI) from the Terra satellite combined with linear regression and the Break for Additive Season and Trend model to reanalyze the spatiotemporal patterns of vegetation change on the TP during 2000–2015. We then quantified the responses of vegetation variation to climatic and anthropogenic factors by coupling climatic and human footprint datasets. Results show that growing season NDVI (GNDVI) values increased significantly overall (0.0011 year−1, p < 0.01) during 2000–2015 and that 70.37% of vegetated area on the TP (23.47% significantly with p < 0.05) exhibited greening trends with the exception of the southwest TP. However, vegetation greenness experienced trend shifts from greening to browning in half of the ecosystem zones occurred around 2010, likely induced by spatially heterogeneous temporal trends of climate variables. The vegetation changes in the northeastern and southwestern TP were water limited, the mid-eastern TP exhibited strong temperature responses, and the south of TP was driven by a combination of temperature and solar radiation. Furthermore, we found that, to some extent, anthropogenic disturbances offset climate-driven vegetation greening and aggravated vegetation browning induced by water deficit. These findings suggest that the impact of anthropogenic activities on vegetation change might not overwhelm that of climate change at the region scale.
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