Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment.

Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast cancer screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast density from digital mammograms. Our method leverages deep learning using two convolutional neural network architectures to accurately segment the breast area. An AI algorithm combining superpixel generation and radiomic machine learning is then applied to differentiate dense from non-dense tissue regions within the breast, from which breast density is estimated. Our method was trained and validated on a multi-racial, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent matched case-control dataset of 6368 digital mammograms (414 cases; 1178 controls) for both breast density estimation and case-control discrimination. On the independent dataset, breast percent density (PD) estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, in a model adjusted for age and BMI, Deep-LIBRA yielded a higher case-control discrimination performance (area under the ROC curve, AUC = 0.612 [95% confidence interval (CI): 0.584, 0.640]) compared to four other widely-used research and commercial breast density assessment methods (AUCs = 0.528 to 0.599). Our results suggest a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods.

Industrial alkali lignin-derived biochar as highly efficient and low-cost adsorption material for Pb(II) from aquatic environment.

Developing a cost-effective and high-efficiency biochar is critical in various environmental applications. Lignin-based materials are natural and abundant adsorbents to heavy metals benefited from their special polyphenol structure and physicochemical properties. In this study, adsorption capacities to Pb(II) by alkali lignin (AL) and its biochar derivative (ALB) were comparatively discussed, and the latter exhibited superior adsorption performance, with a maximum adsorption capacity almost twice that of the former, and a much faster absorption rate. The qm value of ALB was significantly superior to that of other reported biochar materials. Pb(II) was mainly adsorbed into ALB in three forms: mineral precipitation, ion exchange, and surface complexation, with complexation and mineral precipitation being the dominant mechanisms of adsorption. This study demonstrates that alkali-lignin derived biochar is a promising material for the remediation of polluted by Pb(II).