Breast cancer is the most common cancer affecting women and is the second leading cause of cancer related death worldwide. Angiogenesis, the process of new blood vessel development from pre-existing vasculature, has been implicated in the growth, progression, and metastasis of cancer. Tumor angiogenesis has been explored as a key therapeutic target for decades, as the blockade of this process holds the potential to reduce the oxygen and nutrient supplies that are required for tumor growth. However, many existing anti-angiogenic approaches, such as those targeting Vascular Endothelial Growth Factor, Notch, and Angiopoietin signaling, have been associated with severe side-effects, limited survival advantage, and enhanced cancer regrowth rates. To address these setbacks, alternative pathways involved in the regulation of tumor angiogenesis are being explored, including those involving Bone Morphogenetic Protein-9 signaling, the Sonic Hedgehog pathway, Cyclooxygenase-2, p38-mitogen-activated protein kinase, and Chemokine Ligand 18. This review article will introduce the concept of tumor angiogenesis in the context of breast cancer, followed by an overview of current anti-angiogenic therapies, associated resistance mechanisms and novel therapeutic targets.
Purpose Osteophytes are common radiographic markers of osteoarthritis. However, they are not accurately depicted using conventional imaging, thus hampering surgical interventions that rely on pre-operative images. Studies have shown that ultrasound (US) is promising at detecting osteophytes and monitoring the progression of osteoarthritis. Furthermore, three-dimensional (3D) ultrasound reconstructions may offer a means to quantify osteophytes. The purpose of this study was to compare the accuracy of osteophyte depiction in the knee joint between 3D US and conventional computed tomography (CT). Methods Eleven human cadaveric knees were pre-screened for the presence of osteophytes. Three osteoarthritic knees were selected, and then, 3D US and CT images were obtained, segmented, and digitally reconstructed in 3D. After dissection, high-resolution structured light scanner (SLS) images of the joint surfaces were obtained. Surface matching and root mean square (RMS) error analyses of surface distances were performed to assess the accuracy of each modality in capturing osteophytes. The RMS errors were compared between 3D US, CT and SLS models. Results Average RMS error comparisons for 3D US versus SLS and CT versus SLS models were 0.87 mm ± 0.33 mm (average ± standard deviation) and 0.95 mm ± 0.32 mm, respectively. No statistical difference was found between 3D US and CT. Comparative observations of imaging modalities suggested that 3D US better depicted osteophytes with cartilage and fibrocartilage tissue characteristics compared to CT. Conclusion Using 3D US can improve the depiction of osteophytes with a cartilaginous portion compared to CT. It can also provide useful information about the presence and extent of osteophytes. Whilst algorithm improvements for automatic segmentation and registration of US are needed to provide a more robust investigation of osteophyte depiction accuracy, this investigation puts forward the potential application for 3D US in routine diagnostic evaluations and pre-operative planning of osteoarthritis.
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