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可切除肺癌分期及治疗中前哨淋巴结定位的当前创新进展。

Current innovations in sentinel lymph node mapping for the staging and treatment of resectable lung cancer.

作者信息

Hachey Krista J, Colson Yolonda L

机构信息

Brigham & Women's Hospital, Boston, Massachusetts.

Brigham & Women's Hospital, Boston, Massachusetts.

出版信息

Semin Thorac Cardiovasc Surg. 2014 Autumn;26(3):201-9. doi: 10.1053/j.semtcvs.2014.09.001. Epub 2014 Sep 16.

Abstract

Despite surgical resectability, early-stage lung cancer remains a challenge to cure. Survival outcomes are hindered by variable performance of adequate lymphadenectomy and the limitations of current pathologic nodal staging. Sentinel lymph node (SLN) mapping, a mainstay in the management of breast cancer and melanoma, permits targeted nodal sampling for efficient and accurate staging that can influence both intraoperative and adjuvant treatment decisions. Unfortunately, standard SLN identification techniques with blue dye and radiocolloid tracers have not been shown to be reproducible in lung cancer. In more recent years, intraoperative near-infrared image-guided lung SLN mapping has emerged as promising technology for the identification of the tumor-associated lymph nodes most likely to contain metastatic disease. Additionally, the clinical relevance of SLN mapping for lung cancer remains pressing, as the ability to identify micrometastatic disease in SLNs could facilitate trials to assess chemotherapeutic response and the clinical effect of occult nodal disease. This review outlines the status of lung cancer lymphatic mapping and techniques in development that may help close the gap between translational research in this field and routine clinical practice.

摘要

尽管早期肺癌具有手术可切除性,但治愈它仍然是一项挑战。充分的淋巴结清扫效果参差不齐以及当前病理淋巴结分期的局限性阻碍了生存结局。前哨淋巴结(SLN)定位是乳腺癌和黑色素瘤治疗的主要手段,它允许进行靶向淋巴结采样,以实现高效、准确的分期,这会影响术中及辅助治疗决策。不幸的是,使用蓝色染料和放射性胶体示踪剂的标准SLN识别技术在肺癌中尚未被证明具有可重复性。近年来,术中近红外图像引导的肺SLN定位已成为一种有前景的技术,用于识别最有可能含有转移病灶的肿瘤相关淋巴结。此外,SLN定位对肺癌的临床相关性仍然迫切,因为识别SLN中的微转移病灶的能力有助于评估化疗反应和隐匿性淋巴结疾病临床效果的试验。本综述概述了肺癌淋巴绘图的现状以及正在开发的技术,这些技术可能有助于缩小该领域转化研究与常规临床实践之间的差距。

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