Partial sternectomy with reconstruction of a giant cell tumor of the sternum, a case report, Saudi, Arabia.

BACKGROUND: Giant cell tumor (GCT) is a relatively common and locally aggressive benign bone tumor that rarely affects the sternum. CASE PRESENTATION: We report a case of giant cell tumor of the sternum in a 28-year-old Saudi with painful swelling at the lower part of the sternum. Subtotal sternectomy and reconstruction with a neosternum using two layers of proline mesh, a methyl methacrylate prosthesis, and bilateral pectoralis muscle advancement flaps were performed. CONCLUSIONS: Giant cell tumor of the sternum is a rare diagnosis. Surgical resection with negative margins is the ideal management. To avoid defects or instability of the chest wall, reconstruction of the chest wall with neosternum should be considered.

A novel deep mining model for effective knowledge discovery from omics data.

Knowledge discovery from omics data has become a common goal of current approaches to personalised cancer medicine and understanding cancer genotype and phenotype. However, high-throughput biomedical datasets are characterised by high dimensionality and relatively small sample sizes with small signal-to-noise ratios. Extracting and interpreting relevant knowledge from such complex datasets therefore remains a significant challenge for the fields of machine learning and data mining. In this paper, we exploit recent advances in deep learning to mitigate against these limitations on the basis of automatically capturing enough of the meaningful abstractions latent with the available biological samples. Our deep feature learning model is proposed based on a set of non-linear sparse Auto-Encoders that are deliberately constructed in an under-complete manner to detect a small proportion of molecules that can recover a large proportion of variations underlying the data. However, since multiple projections are applied to the input signals, it is hard to interpret which phenotypes were responsible for deriving such predictions. Therefore, we also introduce a novel weight interpretation technique that helps to deconstruct the internal state of such deep learning models to reveal key determinants underlying its latent representations. The outcomes of our experiment provide strong evidence that the proposed deep mining model is able to discover robust biomarkers that are positively and negatively associated with cancers of interest. Since our deep mining model is problem-independent and data-driven, it provides further potential for this research to extend beyond its cognate disciplines.