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The 8th TNM edition for lung cancer: a critical analysis

机译:第八版TNM肺癌:一项关键分析

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摘要

The 8th edition of the tumor, node and metastasis (TNM) classification provides several new categories and for the first time, some prospective data are included. The T (tumor) descriptor is further subdivided with 1 cm increments for T1 and T2 disease. For metastatic disease (M descriptor) the new M1b category comprises patients with only one metastasis in one distant organ, whereas M1c implies multiple distant metastases in one or several organs. There are no changes regarding the nodal map and N component but new categories are suggested for further analysis, subdividing the N1 and N2 descriptors into involvement of single or multiple lymph node stations. The residual tumor (R) classification, related to completeness of resection, was revised in the 7th edition specifically for lung cancer resection and has been maintained in its revised form in the 8th edition. For the first time, a thorough analysis has been made of patients with multiple lung lesions distinguishing four distinct categories. Although prospective data were used for this edition, their overall number is low and more good-quality prospective data collection coming from all continents is certainly required. Main challenge for subsequent editions is the combination of specific anatomical factors with detailed immunohistochemical data and information from biomarkers and mutational changes inside the primary tumor as well as those occurring in lymph node and distant metastases. In this way not only prognosis of our patients with lung cancer will be better determined, but more specific diagnostic and therapeutic algorithms may be applied.
机译:第八版肿瘤,淋巴结转移(TNM)分类提供了几个新类别,并且首次包括了一些前瞻性数据。对于T1和T2疾病,T(肿瘤)描述符进一步细分为1 cm。对于转移性疾病(M描述词),新的M1b类别包括在一个远处器官中只有一个转移的患者,而M1c则隐含在一个或几个器官中的多个远处转移。节点图和N分量没有变化,但建议新的类别进行进一步分析,将N1和N2描述符细分为单个或多个淋巴结站。与切除的完整性有关的残余肿瘤分类在第7版中专门针对肺癌切除术进行了修订,并在第8版中保持了其修订形式。首次对具有多个肺部病变的患者进行了全面分析,将其分为四个不同的类别。尽管此版本使用了前瞻性数据,但它们的总数很低,并且肯定需要来自各大洲的高质量的前瞻性数据收集。后续版本的主要挑战是将特定的解剖学因素与详细的免疫组化数据以及来自生物标志物的信息以及原发性肿瘤内部以及在淋巴结和远处转移中发生的突变的信息结合起来。这样,不仅可以更好地确定我们肺癌患者的预后,而且可以应用更具体的诊断和治疗算法。

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