目的:探讨转移性骨肿瘤引起病理性骨折的诊断、治疗与预防。方法:收集34例因各类转移性骨肿瘤引起病理性骨折患者的资料。其中,6例患者因肺癌、肝癌或乳腺癌引起全身多处骨转移和多器官转移,分别行胫骨结节骨牵引和肩关节贴胸位治疗;另2...目的:探讨转移性骨肿瘤引起病理性骨折的诊断、治疗与预防。方法:收集34例因各类转移性骨肿瘤引起病理性骨折患者的资料。其中,6例患者因肺癌、肝癌或乳腺癌引起全身多处骨转移和多器官转移,分别行胫骨结节骨牵引和肩关节贴胸位治疗;另28例患者行手术治疗,其中肺癌转移9例、乳腺癌转移6例、肾癌转移5例、甲状腺癌转移4例、肝癌转移2例、胰腺癌和前列腺癌转移各1例,分别行人工股骨头置换术、闭合复位、动力髋螺钉(dynamic hip screw,DHS)及股骨和肱骨髓内钉内固定和钢板内固定术。34例患者均获得随访。结果:6例未行手术治疗患者,因多器官转移等其他并发症于2个月至1年内死亡,但骨折部位表现出临床愈合、无疼痛,肢体关节活动良好。28例手术患者中,8例患者在随访期间死于多器官转移等其他并发症,其余患者经化疗、放疗及中西医结合治疗后至今存活,骨折处平均于术后3个月出现临床愈合,肢体功能恢复良好。结论:在诊断转移性肿瘤引起的病理性骨折时,应首先防止漏诊,明确转移肿瘤的来源;同时根据患者的年龄、受伤机制、影像学表现及病理检查结果进行综合判断。在处理转移性骨肿瘤的病理性骨折时,应以减轻患者痛苦、提高生活质量为主要目的。展开更多
During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution qual...During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.展开更多
文摘目的:探讨转移性骨肿瘤引起病理性骨折的诊断、治疗与预防。方法:收集34例因各类转移性骨肿瘤引起病理性骨折患者的资料。其中,6例患者因肺癌、肝癌或乳腺癌引起全身多处骨转移和多器官转移,分别行胫骨结节骨牵引和肩关节贴胸位治疗;另28例患者行手术治疗,其中肺癌转移9例、乳腺癌转移6例、肾癌转移5例、甲状腺癌转移4例、肝癌转移2例、胰腺癌和前列腺癌转移各1例,分别行人工股骨头置换术、闭合复位、动力髋螺钉(dynamic hip screw,DHS)及股骨和肱骨髓内钉内固定和钢板内固定术。34例患者均获得随访。结果:6例未行手术治疗患者,因多器官转移等其他并发症于2个月至1年内死亡,但骨折部位表现出临床愈合、无疼痛,肢体关节活动良好。28例手术患者中,8例患者在随访期间死于多器官转移等其他并发症,其余患者经化疗、放疗及中西医结合治疗后至今存活,骨折处平均于术后3个月出现临床愈合,肢体功能恢复良好。结论:在诊断转移性肿瘤引起的病理性骨折时,应首先防止漏诊,明确转移肿瘤的来源;同时根据患者的年龄、受伤机制、影像学表现及病理检查结果进行综合判断。在处理转移性骨肿瘤的病理性骨折时,应以减轻患者痛苦、提高生活质量为主要目的。
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.
