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Predicting the Value of Adjuvant Therapy in Esophageal Squamous Cell Carcinoma by Combining the Total Number of Examined Lymph Nodes with the Positive Lymph Node Ratio

Yida Li MD, Weixin Zhao MD, Jianjiao Ni MD, Liqing Zou MD, Xi Yang MD, Weiwei Yu MD, Xiaolong Fu MD, Kuaile Zhao MD, Yawei Zhang MD, Haiquan Chen MD, Jiaqing Xiang MD, Congying Xie MD, Zhengfei Zhu MD
Thoracic Oncology
Volume 26, Issue 8 / August , 2019

Abstract

Background

The value of adjuvant therapy for esophageal squamous cell carcinoma (ESCC) has been controversial, at least partially due to the lack of efficient criteria for selecting suitable patients. This study aimed to explore the existence of parameters related to lymph node (LN) status that can predict the value of adjuvant therapy in ESCC.

Methods

The study included 298 patients with ESCC who had undergone radical esophagectomy with lymphadenectomy. Adjuvant therapy was defined as reception of adjuvant chemotherapy, radiotherapy, or chemoradiotherapy. For the study, LN ratio (LNR), total number of resected LNs (TLNs), and pN stage were selected for Cox regression analyses, including their correlations and prognostic values for survival. Log-rank tests were used to compare the survival rates of the patients with and without adjuvant therapy stratified by pN stage, TLNs, LNR, or their combinations.

Results

The independent prognostic factors for survival were TLNs, LNR, and pN stage. Whereas pN stage was significantly related to TLNs and LNR, TLNs were not correlated with LNR. The survival rates between the patients with and those without adjuvant therapy stratified by pN stage, TLNs, or LNR did not differ significantly. We used the median values of TLNs and LNR to group the patients into four groups. The patients in the group with fewer TLNs and higher LNR who had undergone adjuvant therapy showed a significantly better survival than those without adjuvant therapy (p = 0.030).

Conclusions

In contrast to TLNs, LNR, and pN stage as single factors, the combination of TLNs and LNR can predict the value of adjuvant therapy.

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