Background: Microcalcification is one of the most reliable clinical features of the malignancy risk of breast cancer, and it is associated with enhanced tumour aggressiveness and poor prognosis. However, its underlying molecular mechanism remains unclear. Methods: Clinical data were retrieved to analyse the association between calcification and bone metastasis in patients with breast cancer. Using multiple human breast cancer cell lines, the osteogenic cocktail model was established in vitro to demonstrate calcification-exacerbated metastasis. Migration and invasion characteristics were determined by wound healing and transwell migration. mRNA and protein expression were identified by quantitative PCR and western blotting. Metabolic alterations in breast cancer cells were evaluated using Seahorse Analyser. Results: The osteogenic differentiation of human breast cancer cells activated the classical TGF-β/Smad signalling pathway and the non-canonical MAPK pathway, which, in turn, exacerbated the progression of epithelial–mesenchymal transition (EMT). The metabolic programme switched to enhancing mitochondrial oxidative phosphorylation (OXPHOS) upon osteogenic differentiation. Rotenone was used to inhibit the OXPHOS complex during osteogenesis to block mitochondrial function, consequently reversing the EMT phenotype. Conclusions: This study provides important insights into the mechanisms involved in breast cancer bone metastasis, and outlines a possible strategy to intervene in OXPHOS for the treatment of breast tumours.

Synthetic digital mammography (SDM), a 2D image generated from digital breast tomosynthesis (DBT), is used as a potential substitute for full-field digital mammography (FFDM) in clinic to reduce the radiation dose for breast cancer screening. Previous studies exploited projection geometry and fused projection data and DBT volume, with different post-processing techniques applied on re-projection data which may generate different image appearance compared to FFDM. To alleviate this issue, one possible solution to generate an SDM image is using a learning-based method to model the transformation from the DBT volume to the FFDM image using current DBT/FFDM combo images. In this study, we proposed to use a deep convolutional neural network (DCNN) to learn the transformation to generate SDM using current DBT/FFDM combo images. Gradient guided conditional generative adversarial networks (GGGAN) objective function was designed to preserve subtle MCs and the perceptual loss was exploited to improve the performance of the proposed DCNN on perceptual quality. We used various image quality criteria for evaluation, including preserving masses and MCs which are important in mammogram. Experiment results demonstrated progressive performance improvement of network using different objective functions in terms of those image quality criteria. The methodology we exploited in the SDM generation task to analyze and progressively improve image quality by designing objective functions may be helpful to other image generation tasks.

Purpose: To establish an ensemble machine learning (ML) model for predicting the risk of futile recanalization, malignant cerebral edema (MCE), and cerebral herniation (CH) in patients with acute ischemic stroke (AIS) who underwent mechanical thrombectomy (MT) and recanalization.Methods: This prospective study included 110 patients with premorbid mRS ≤ 2 who met the inclusion criteria. Futile recanalization was defined as a 90-day modified Rankin Scale score >2. Clinical and imaging data were used to construct five ML models that were fused into a logistic regression algorithm using the stacking method (LR-Stacking). We added the Shapley Additive Explanation method to display crucial factors and explain the decision process of models for each patient. Prediction performances were compared using area under the receiver operating characteristic curve (AUC), F1-score, and decision curve analysis (DCA).Results: A total of 61 patients (55.5%) experienced futile recanalization, and 34 (30.9%) and 22 (20.0%) patients developed MCE and CH, respectively. In test set, the AUCs for the LR-Stacking model were 0.949, 0.885, and 0.904 for the three outcomes mentioned above. The F1-scores were 0.882, 0.895, and 0.909, respectively. The DCA showed that the LR-Stacking model provided more net benefits for predicting MCE and CH. The most important factors were the hypodensity volume and proportion in the corresponding vascular supply area.Conclusion: Using the ensemble ML model to analyze the clinical and imaging data of AIS patients with successful recanalization at admission and within 24 h after MT allowed for accurately predicting the risks of futile recanalization, MCE, and CH.