Clinicopathological investigation of lymph node metastasis predictors in superficial esophageal squamous cell carcinoma with a focus on evaluation of lympho-vascular invasion
Clinicopathological investigation of lymph node metastasis predictors in superficial esophageal squamous cell carcinoma with a focus on evaluation of lympho-vascular invasion
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DOI：
10.3109/00365521.2013.832365
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发表时间：
2013-10-01
影响因子：
1.9
通讯作者：
Tajiri, Hisao
中科院分区：
文献类型：
--
作者：
Mitobe, Jimi;Ikegami, Masahiro;Tajiri, Hisao
Objective.浅表性食管鳞状细胞癌（sESCC：粘膜内和粘膜下浸润性癌）淋巴结转移（LNM）风险的可靠指标可能有助于帮助制定治疗sESCC的最佳临床决策。在食管癌中，即使在sESCC中也存在转移的可能性，在进行病理诊断时需要仔细评估。在这项研究中，我们客观地评估了LNM的预测因素。材料和方法。共获得110例连续sESCC病例。我们评估了LNM的候选预测因素如下：（1）最大肿瘤直径;（2）肉眼类型;（3）肿瘤浸润深度;（4）组织学分化;（5）浸润性生长模式;（6）肿瘤出芽;（7）淋巴管浸润;（8）静脉浸润和（9）淋巴血管浸润（LVI）。采用Elastica-Van Gieson染色（EVG）和免疫组化（IHC：D2-40、CD 31、CD 34）来评估对淋巴-血管间隙的浸润。对于统计分析，进行了单因素和多因素logistic回归。结果37例（33.6%）观察到LNM。使用EVG和IHC的LVI是LNM最强的独立预测因子，优势比为12.01。用EVG、IHC和LNM分析LVI之间的关系显示阴性预测值为94.6%。结论.采用EVG和IHC评估LVI可能有助于预测sESCC的LNM。
Objective. Reliable indicators of the risk of lymph node metastasis (LNM) in superficial esophageal squamous cell carcinoma (sESCC: intramucosal and submucosal invasive carcinoma) may contribute to assist optimal clinical decision-making for treating sESCC. In esophageal cancer, there is a possibility of metastasis, even in sESCC, and careful evaluation is needed when making a pathological diagnosis. In this study, we objectively evaluated predictive factors of LNM. Materials and methods. A total of 110 consecutive sESCC cases were obtained. We evaluated candidate predictive factors of LNM as follows: (1) maximum tumor diameter; (2) macroscopic type; (3) depth of tumor invasion; (4) histological differentiation; (5) infiltrative growth pattern; (6) tumor budding; (7) lymphatic invasion; (8) venous invasion and (9) lympho-vascular invasion (LVI). Both Elastica-Van Gieson staining (EVG) and immunohistochemistry (IHC: D2-40, CD31, CD34) were used to evaluate invasion into the lympho-vascular spaces. For statistical analyses, single and multiple logistic regression were performed. Results. LNM was observed in 37 cases (33.6%). LVI using EVG and IHC was the strongest independent predictor of LNM with an odds ratio of 12.01. Analysis of the relationship between LVI using EVG and IHC and LNM showed a negative predictive value of 94.6%. Conclusions. Evaluation of LVI using EVG and IHC may contribute to predict LNM in sESCC.