Epithelial cancers including breast and prostate commonly progress to form incurable bone metastases. For this to occur, cancer cells must adapt their phenotype and behaviour to enable detachment from the primary tumour, invasion into the vasculature, and homing to and subsequent colonisation of bone. It is widely accepted that the metastatic process is driven by the transformation of cancer cells from a sessile epithelial to a motile mesenchymal phenotype through epithelial-mesenchymal transition (EMT). Dissemination of these motile cells into the circulation provides the conduit for cells to metastasise to distant organs. However, accumulating evidence suggests that EMT is not sufficient for metastasis to occur and that specific tissue-homing factors are required for tumour cells to lodge and grow in bone. Once tumour cells are disseminated in the bone environment, they can revert into an epithelial phenotype through the reverse process of mesenchymal-epithelial transition (MET) and form secondary tumours. In this review, we describe the molecular alterations undertaken by breast cancer cells at each stage of the metastatic cascade and discuss how these changes facilitate bone metastasis.

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It is significant to classify teeth categories in dental treatment. A novel teeth classification method was proposed in this paper, which combined fractional Fourier entropy and feedforward neural network. Firstly, fractional Fourier transform was performed on the teeth CT images and the obtained spectrums were used to extract entropies as the features. Then, a feedforward neural network was employed for automatic classification. To train the parameters in the network, improved hybrid genetic algorithm was leveraged. Experiment results suggested that our method achieved state-of-the-art performance.