AIM： To evaluate the clinical outcome of Ivor Lewis subtotal esophagectomy with two-field lymphadenectomy for patients with squamous cell carcinoma of the lower thoracic esophagus. METHODS： From January 1998 to Dece...AIM： To evaluate the clinical outcome of Ivor Lewis subtotal esophagectomy with two-field lymphadenectomy for patients with squamous cell carcinoma of the lower thoracic esophagus. METHODS： From January 1998 to December 2001, 73 patients with lower thoracic esophageal carcinoma underwent Ivor-Lewis subtotal esophagectomy with two-field lymphadenectomy. Clinicopathological information, postoperative complications, mortality and long term survival of all these patients were analyzed retrospectively. RESULTS： The operative morbidity and mortality was 15.1% and the mortality was 2.7%. Lymph node metastases were found in 52 patients （71.2%）. Nodal metastases to the upper, middle, lower mediastini and upper abdomen were found in 13 （17.8%）, 15 （20.5%）, 30 （41.1%）, and 25 （34.2%） patients, respectively. Postoperative staging was as follows： stage Ⅰ in 5 patients, stage Ⅱ in 34 patients, stage Ⅲ in 32 patients, and stage Ⅳ in 2 patients, respectively. The overall 5-year survival rate was 23.3%. For NO and N1 patients, the 5-year survival rate was 38.1% and 17.3%, respectively （X^2 = 22.65, P 〈 0.01）. The 5-year survival rate for patients in stages Ⅱ a, Ⅱ b and Ⅲ was 31.2%, 27.8% and 12.5%, repsectively （X^2 = 29.18, P 〈 0.01）. CONCLUSION： Ivor Lewis subtotal esophagectomy with two-field （total mediastinum） lymphadenectomy is a safe and appropriate operation for squamous cell carcinoma of the lower thoracic esophagus.展开更多
针对RRT(Rapidly-exploring Random Tree)算法在机器人路径规划过程存在采样点随机性高、算法效率低、路径规划时间长以及规划路径冗长等问题,文中提出一种结合人工势场法的双向RRT路径规划算法。将传统RRT算法中单向扩展方式改为由起...针对RRT(Rapidly-exploring Random Tree)算法在机器人路径规划过程存在采样点随机性高、算法效率低、路径规划时间长以及规划路径冗长等问题,文中提出一种结合人工势场法的双向RRT路径规划算法。将传统RRT算法中单向扩展方式改为由起点和终点同时进行扩展,在节点扩展时加入人工势场法进行引导,增加节点扩展的目的性。将固定步长改换为可变步长,使随机树可以更快地向目标点扩展。对生成路径进行剪枝处理,删除路径中的冗余节点,进一步缩短路径长度。利用MATLAB仿真平台在相同环境下对比所提改进算法与RRT-Connect算法、DRRT-Connect(Dynamic Rapidly-exploring Random Tree Connect)算法、GB(Goal-Biased)-RRT算法、A^(*)算法、PRM(Probabilistic Road Map)算法的路径规划效果。仿真结果表明,所提改进算法与其他改进算法相比最短路径缩短了7%,最短搜索时间降低了65%,提高了算法的规划效率。将所提算法应用于机器人,结果证明了其具有较强可行性。展开更多
文摘AIM： To evaluate the clinical outcome of Ivor Lewis subtotal esophagectomy with two-field lymphadenectomy for patients with squamous cell carcinoma of the lower thoracic esophagus. METHODS： From January 1998 to December 2001, 73 patients with lower thoracic esophageal carcinoma underwent Ivor-Lewis subtotal esophagectomy with two-field lymphadenectomy. Clinicopathological information, postoperative complications, mortality and long term survival of all these patients were analyzed retrospectively. RESULTS： The operative morbidity and mortality was 15.1% and the mortality was 2.7%. Lymph node metastases were found in 52 patients （71.2%）. Nodal metastases to the upper, middle, lower mediastini and upper abdomen were found in 13 （17.8%）, 15 （20.5%）, 30 （41.1%）, and 25 （34.2%） patients, respectively. Postoperative staging was as follows： stage Ⅰ in 5 patients, stage Ⅱ in 34 patients, stage Ⅲ in 32 patients, and stage Ⅳ in 2 patients, respectively. The overall 5-year survival rate was 23.3%. For NO and N1 patients, the 5-year survival rate was 38.1% and 17.3%, respectively （X^2 = 22.65, P 〈 0.01）. The 5-year survival rate for patients in stages Ⅱ a, Ⅱ b and Ⅲ was 31.2%, 27.8% and 12.5%, repsectively （X^2 = 29.18, P 〈 0.01）. CONCLUSION： Ivor Lewis subtotal esophagectomy with two-field （total mediastinum） lymphadenectomy is a safe and appropriate operation for squamous cell carcinoma of the lower thoracic esophagus.
文摘针对RRT(Rapidly-exploring Random Tree)算法在机器人路径规划过程存在采样点随机性高、算法效率低、路径规划时间长以及规划路径冗长等问题,文中提出一种结合人工势场法的双向RRT路径规划算法。将传统RRT算法中单向扩展方式改为由起点和终点同时进行扩展,在节点扩展时加入人工势场法进行引导,增加节点扩展的目的性。将固定步长改换为可变步长,使随机树可以更快地向目标点扩展。对生成路径进行剪枝处理,删除路径中的冗余节点,进一步缩短路径长度。利用MATLAB仿真平台在相同环境下对比所提改进算法与RRT-Connect算法、DRRT-Connect(Dynamic Rapidly-exploring Random Tree Connect)算法、GB(Goal-Biased)-RRT算法、A^(*)算法、PRM(Probabilistic Road Map)算法的路径规划效果。仿真结果表明,所提改进算法与其他改进算法相比最短路径缩短了7%,最短搜索时间降低了65%,提高了算法的规划效率。将所提算法应用于机器人,结果证明了其具有较强可行性。
