The aim of this study was to assess a specific protocol for the treatment of patients with a parosteal osteosarcoma of the distal femur with limb salvage involving hemicortical resection and reconstruction using recycled pasteurised autograft and internal fixation. Between January 2000 and January 2010, 13 patients with a mean age of 26.5 years (17 to 39) underwent this procedure. All the tumours were staged according to Enneking's criteria: there were eight stage IA tumours and five stage IB tumours. The mean follow-up was 101.6 months (58 to 142), and mean post-operative Musculoskeletal Tumour Society functional score was 88.6% (80% to 100%) at the final follow-up. All the patients had achieved bony union; the mean time to union was 11.2 months (6 to 18). Local recurrence occurred in one patient 27 months post-operatively. No patient had a pulmonary metastasis. A hemicortical procedure for the treatment of a parosteal osteosarcoma is safe and effective. Precise pre-operative planning using MRI is essential in order to define the margins of resection. Although it is a technically demanding procedure, gratifying results make it worthwhile for selected patients.

Quantitative prediction of cellular responses to drugs and drug combinations is a challenging and valuable topic in pharmaceutical research. In the past decade, microarray technology has become a routine tool for monitoring genome-wide expression changes and has been widely adopted for exploring drug response in the pharmaceutical field. However, how to predict the synergistic effect of drug combinations using microarray data is a challenging task. In this article, we report a simple prediction framework based on the genome-wide and quantitative profiling of cellular responses to individual drugs. By exploring the differential expression profiles, our correlation-based strategy can reveal the synergistic effects of drug combinations. The comparison with gold-standard experimental results demonstrates the strengths and weaknesses in relation to prediction based only on cellular response to individual drugs. Specifically, the prediction strategy may work for a drug combination whose individual drugs show related transcriptomic mechanisms but not for others.