Getting precise locations of target tumors can help to ensure ablation of cancerous tissues and avoid unwanted destruction of healthy tissues in high-intensity focused ultrasound (HIFU) treatment system. Because of speckle noise and spurious boundaries in ultrasound images, traditional image segmentation methods are not suitable for achieving the precise locations of target tumors in HIFU ablation. In this paper, a multi-step directional generalized gradient vector flow snake model is introduced for target tumor segmentation. In the first step, the traditional generalized gradient vector flow (GGVF) snake is used to obtain an approximate contour of the tumor. According to the approximate contour, a new distance map is generated. Subsequently, a new directional edge map is created by calculating a scalar product of the gradients of the distance map and the initial image. In this process, the gradient directional information and the magnitude information of the distance map are used to attenuate unwanted edges and highlight the real edges in the new directional edge map. Finally, a refined GGVF field is derived from a diffusion operation of the gradient vectors of the directional edge map. The GGVF field is used to refine the tumor's contour, by directing the approximate contour to edges with the desired gradient directionality. Based on the newly developed snake model, the influences of the spurious boundaries and the speckle noise are significantly reduced in the ultrasound image segmentation. Experimental results indicate that this technique is greatly useful for target tumor segmentation in HIFU treatment system 相似文献
Glypican-5 (GPC5) belongs to the glypican family of proteoglycans that have been implicated in a variety of physiological processes, ranging from cell proliferation to morphogenesis. However, the role of GPC5 in human cancer remains poorly understood. We report that knockdown of GPC5 in bronchial epithelial cells promoted, and forced expression of GPC5 in non-small lung cancer (NSCLC) cells suppressed, the anchorage-independent cell growth. In vivo, expression of GPC5 inhibited xenograft tumor growth of NSCLC cells. Furthermore, we found that GPC5 was expressed predominantly as a membrane protein, and its expression led to diminished phosphorylation of several oncogenic receptor tyrosine kinases, including the ERBB family members ERBB2 and ERBB3, which play critical roles in lung tumorigenesis. Collectively, our results suggest that GPC5 may act as a tumor suppressor, and reagents that activate GPC5 may be useful for treating NSCLC. 相似文献
Thirty-one different 3-O-acetyl-OA derived amides have been prepared and screened for their cytotoxic activity. In the SRB assays nearly all the carboxamides displayed good cytotoxicity in the low μM range for several human tumor cell lines. Low EC50 values were obtained especially for the picolinylamides 14–16, for a N-[2-(dimethylamino)-ethyl] derivative 27 and a N-[2-(pyrrolinyl)-ethyl] carboxamide 28. These compounds were submitted to an extensive biological testing and proved compound 15 to act mainly by an arrest of the tumor cells in the S phase of the cell cycle. Cell death occurred by autophagy while compounds 27 and 28 triggered apoptosis. 相似文献
Introduction: Despite extreme genetic heterogeneity, tumors often show similar alterations in the expression, stability, and activation of proteins important in oncogenic signaling pathways. Thus, classifying tumor samples according to shared proteomic features may help facilitate the identification of cancer subtypes predictive of therapeutic responses and prognostic for patient outcomes. Meanwhile, understanding mechanisms of intrinsic and acquired resistance to anti-cancer therapies at the protein level may prove crucial to devising reversal strategies.
Areas covered: Herein, we review recent advances in quantitative proteomic technology and their applications in studies to identify intrinsic tumor subtypes of various tumors, to illuminate mechanistic aspects of pharmacological and oncogenic adaptations, and to highlight interaction targets for anti-cancer compounds and cancer-addicted proteins.
Expert commentary: Quantitative proteomic technologies are being successfully employed to classify tumor samples into distinct intrinsic subtypes, to improve existing DNA/RNA based classification methods, and to evaluate the activation status of key signaling pathways. 相似文献