Prostate brachytherapy, a treatment for prostate cancer, was a procedure that typically involved placing radioactive sources in a cancerous prostate using percutaneous needles. The placement of these sources determined the dose that the prostate and healthy tissues surrounding it received. However, because a needle could bend, tissue could deform, and a patient could move, each source may have been displaced from its planned position. This source misplacement could later cause some cancer to be spared or healthy organs to be further damaged. To better understand patterns of source misplacement, and eventually reduce the phenomenon, this work matched and registered implanted sources with their planned positions. Each implant was registered to its plan using a sequence of four successive registrations. A rough initial registration was first found, using features known in the planned dataset and estimated from the implanted dataset. Second, subsets of sources were reconstructed in the implanted dataset. The implanted sources were next matched to the planned sources using the subsets as constraints. Finally, the optimal rigid transformation between the implants and the plan was found. The algorithm was tested on both simulated and clinical datasets. Simulations placed limits on how properties of the subsets affected registration accuracy. When tested on 9 clinical datasets, the algorithm found 100% of correct plan-implant source matches within seconds on commonly available computers. When the implanted strands were reconstructed as sine waves, 97% of t strands had an amplitude of less than 2mm. The clinical accuracy result generally agreed with simulation: subsets with amplitudes less than 2mm were expected to produce an accuracy >90%. The high accuracy of the algorithm may enable its use in finding patterns of source misplacement. The fast run-time of the algorithm may additionally make it useful for use in a clinical setting. / Thesis (Master, Computing) -- Queen's University, 2011-12-23 13:28:07.348

No / Triple-negative breast cancers (TNBC) are clinically heterogeneous, an aggressive form of breast cancer with poor diagnosis and highly therapeutic resistant. It is urgently needed for identifying novel biomarkers with increased sensitivity and specificity for early detection and personalised therapeutic intervention. Microarray profiling offered significant advances in molecular classification but sample scarcity and cohort heterogeneity remains challenging areas. Here, we investigated diagnostics signatures derived from human triple-negative tissue. We applied REMARK criteria for the selection of relevant studies and compared the signatures gene lists directly as well as assessed their classification performance in predicting diagnosis using leave-one-out cross-validation. The cross-validation results shows excellent classification accuracy ratios using all data sets. A subset signature (17-gene) extracted from the convergence of eligible signatures have also achieved excellent classification accuracy of 89.37% across all data sets. We also applied gene ontology functional enrichment analysis to extract potentially biological process, pathways and network involved in TNBC disease progression. Through functional analysis, we recognized that these independent signatures have displayed commonalities in functional pathways of cell signaling, which play important role in the development and progression of TNBC. We have also identified five unique TNBC pathways genes (SYNCRIP, NFIB, RGS4, UGCG, LOX and NNMT), which could be important for therapeutic interventions as indicated by their close association with known drivers of TNBC and previously published experimental studies. / Yorkshire Cancer Research for the Supplementary ort of CWS (BPP049 and B209PG)