Background Breast cancer is the most common malignancy in women, and it is also the leading cause of death in female patients; the most common pathological type of BC is infiltrating duct carcinoma (IDC). Some nomograms have been developed to predict bone metastasis (BM) in patients with breast cancer. However, there are no studies on diagnostic and prognostic nomograms for BM in newly diagnosed IDC patients. Methods IDC patients with newly diagnosed BM from 2010 to 2016 in the Surveillance, Epidemiology and End Results (SEER) database were reviewed. Multivariate logistic regression analysis was used to identify risk factors for BM in patients with IDC. Univariate and multivariate Cox proportional hazards regression analysis were used to explore the prognostic factors of BM in patients with IDC. We then constructed nomograms to predict the risk and prognosis of BM for patients with IDC. The results were validated using bootstrap resampling and retrospective research on 113 IDC patients with BM from 2015 to 2018 at the Affiliated Hospital of Chengde Medical University. Results This study included 141,959 patients diagnosed with IDC in the SEER database, of whom 2383 cases were IDC patients with BM. The risk factors for BM in patients with IDC included sex, primary site, grade, T stage, N stage, liver metastasis, race, brain metastasis, breast cancer subtype, lung metastasis, insurance status, and marital status. The independent prognostic factors were brain metastases, race, grade, surgery, chemotherapy, age, liver metastases, breast cancer subtype, insurance status, and marital status. Through calibration, receiver operating characteristic curve and decision curve analyses, we found that the nomogram for predicting the prognosis of IDC patients with BM displayed great performance both internally and externally. Conclusion These nomograms are expected to be a precise and personalized tool for predicting the risk and prognosis for BM in patients with IDC. This will help clinicians develop more rational and effective treatment strategies.

Purpose To design a new deep learning network for fast and accurate water–fat separation by exploring the correlations between multiple echoes in multi‐echo gradient‐recalled echo (mGRE) sequence and evaluate the generalization capabilities of the network for different echo times, field inhomogeneities, and imaging regions. Methods A new multi‐echo bidirectional convolutional residual network (MEBCRN) was designed to separate water and fat images in a fast and accurate manner for the mGRE data. This new MEBCRN network contains 2 main modules, the first 1 is the feature extraction module, which learns the correlations between consecutive echoes, and the other one is the water–fat separation module that processes the feature information extracted from the feature extraction module. The multi‐layer feature fusion (MLFF) mechanism and residual structure were adopted in the water–fat separation module to increase separation accuracy and robustness. Moreover, we trained the network using in vivo abdomen images and tested it on the abdomen, knee, and wrist images. Results The results showed that the proposed network could separate water and fat images accurately. The comparison of the proposed network and other deep learning methods shows the advantage in both quantitative metrics and robustness for different TEs, field inhomogeneities, and images acquired for various imaging regions. Conclusion The proposed network could learn the correlations between consecutive echoes and separate water and fat images effectively. The deep learning method has certain generalization capabilities for TEs and field inhomogeneity. Although the network was trained only in vivo abdomen images, it could be applied for different imaging regions.

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Dissolved and particle-associated polycyclic aromatic hydrocarbons (PAHs) were measured in the surface microlayer (SM) of the York and Elizabeth River tributaries of Southern Chesapeake Bay throughout 1 year. Particle-associated PAH concentrations in the SM increased nonlinearly with total suspended particulate (TSP) levels, and a much more rapid rise in particulate PAHs with TSP was observed in the Elizabeth River vs the York River SM. The steep rise in particle-associated PAH concentrations along with observed nonequilibrium distributions of PAHs between the dissolved and particle phases in the Elizabeth River SM implies that atmospheric deposition of soot particles may be an important source of PAHs to this urban estuary. A significant relationship between dissolved PAHs and dissolved organic carbon (DOC) was also observed at the York River site, with the more hydrophilic PAHs exhibiting greater increases with DOC. PAHs were significantly enriched in the SM of both sites relative to the subsurface water concentrations. Particle-associated and dissolved phase enrichment factors were strongly influenced by wind speed at the Elizabeth River site; however, only particulate phase enrichment factors were dependent on wind speed at the York River site.

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