High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4.

BACKGROUND: BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions. METHODS: This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including Ktrans, Kep, Ve, and Vp, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_Ktrans, Kep, and Vp; Peri_Ktrans, Kep, and Vp) and from L_DCE-MRI (Lesion_Kep, Peri_Vp, BPE_Ktrans and BPE_Vp) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_Ktrans (AUC = 0.866), Lesion_Kep (AUC = 0.929), Lesion_Vp (AUC = 0.872), Peri_Ktrans (AUC = 0.733), Peri_Kep (AUC = 0.810), and Peri_Vp (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_Kep (AUC = 0.767), Peri_Vp (AUC = 0.726), and BPE_Ktrans and BPE_Vp (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_Kep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_Kep in the H_DCE-MRI group and the senior radiologist (P = 0.04). CONCLUSIONS: Pharmacokinetic parameters (Ktrans, Kep and Vp) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional Kep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.

Sustainability performance evaluation involving internal resource imbalance: an additive aggregation network DEA approach.

Environmental pollution, as a byproduct of economic growth, causes negative pressure on human health. Its sustainability management performance is closely bound up with the ecological carrying capacity. Due to the limited carrying capacity of ecosystems to pollutants, the hidden costs of pollutants may increase when pollutants flow and spread. This is an external manifestation of the internal resource imbalance within the ecosystem, restricting the sustainability of economy and environment and overlooked by most studies that target sustainability performance evaluation. Thus, this study considers the internal resource imbalance during the sustainability performance evaluation for the first time in the context of the interaction among economy, environment, and human health, by constructing a production-treatment-health framework, proposing an internal resource imbalance index and developing an additive aggregation network data envelopment analysis model with the semidefinite programming technology. This study takes 30 Chinese provinces from 2012 to 2017 as the research objects and confirms the effectiveness of sustainability management in terms of water pollution purification and water ecological construction.

Measuring environmental efficiency and technology inequality of China's power sector: methodological comparisons among data envelopment analysis, free disposable hull, and super free disposable hull models.

China's power sector has received great research attention because of its large energy consumption and CO2 emissions. This study assesses the environmental efficiency and technology inequality of China's power sector from 2008 to 2017. Methodologically, this study proposes a non-radial FDH (free disposable hull) model and a super non-radial FDH model. The non-radial FDH model relaxes the convex assumption and captures all inefficiencies of inputs, desirable output and undesirable output. The super non-radial model is capable of discriminating efficient power sectors and always has feasible solutions. We also compare their performance with the non-radial data envelopment analysis (DEA) model. The main conclusions are summarized as follows: First, the environmental efficiency of China's power sector has experienced steady growth; the power sectors in the east region outperform those in other regions. Second, the proposed FDH models are more applicable and reliable than the non-radial DEA model in efficiency measurement of China's power sector, due to the indivisibility of labor. Third, there has been growing technology inequality and the main driving factor determining technology inequality is the inter-region efficiency Gini coefficient. To improve environmental efficiency and eliminate technology inequality, the government should mainly solve the issue of excessive labor input and establish a free technology market for technology trading.