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A new classification of lymph node metastases according to the lymph node stations for predicting prognosis in surgical patients with esophageal squamous cell carcinoma.

ABSTRACT: Lymph node metastasis (LNM) is one of the major prognostic factors for esophageal squamous cell carcinoma (ESCC). However there is no consensus regarding the prognostic significance of the location of LNM. Therefore, a novel classification was proposed to identify the lymph node (LN) stations which may be useful in predicting prognosis. A total of 260 ESCC patients were enrolled in this prospective study. The prognostic values of LNM in different lymph node (LN) stations were evaluated by random survival forests (RSF). Their prognostic significance was examined by Cox regression and receiver operating characteristic curve (ROC). The three most frequently involved LN stations were station 16 (24.49%), station 1 (22.22%) and station 2 (21.05%). Stations 1, 2, 8M, 8L and 16 were grouped as dominant LN stations (DLNS) which showed higher values in predicting overall survival (OS) and disease-free survival (DFS) than the remaining LN stations, which we define as non-dominant LN stations (N-DLNS). LNM features of DLNS (number of positive LN stations, number of positive LNs and LN ratio), but not those from N-DLNS, served as independent prognostic factors (P<0.05) whenever used alone or when combined with factors from N-DLNS. Furthermore, the area under ROC indicated that DLNS is a more accurate prediction than N-DLNS (P<0.05). This study demonstrated the value of LNM in DLNS in predicting prognosis in surgical ESCC patients, which outperformed those from N-DLNS. Therefore, the method of dominant and non-dominant classification may serve as an additional parameter to improve individualized therapeutic strategies.

SUBMITTER: Lin Z

PROVIDER: S-EPMC5342812 | biostudies-other | 2016 Nov

REPOSITORIES: biostudies-other

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A new classification of lymph node metastases according to the lymph node stations for predicting prognosis in surgical patients with esophageal squamous cell carcinoma.

Lin Zheng Z Chen Weilin W Chen Yuanmei Y Peng Xiane X Zhu Kunshou K Lin Yimin Y Lin Qiaokuang Q Hu Zhijian Z

Oncotarget 20161101 46

Lymph node metastasis (LNM) is one of the major prognostic factors for esophageal squamous cell carcinoma (ESCC). However there is no consensus regarding the prognostic significance of the location of LNM. Therefore, a novel classification was proposed to identify the lymph node (LN) stations which may be useful in predicting prognosis. A total of 260 ESCC patients were enrolled in this prospective study. The prognostic values of LNM in different lymph node (LN) stations were evaluated by random ...[more]

PMID: 27788489

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Project description:BackgroundThe site of lymph node metastasis (LNM) may affect the prognosis of patients with esophageal squamous cell carcinoma (ESCC). To investigate the prognoses of pararespiratory and paradigestive LNM and to propose a novel N (nN) staging system that integrates both the LNM site and count.MethodsThis study was a multicenter, large-sample, retrospective cohort study that included ESCC patients with LNM between January 2014 and December 2019 from three Chinese institutes. Patients were set into training (two institutes) and external validation (one institute) cohorts. The primary outcomes were survival differences in LNM site and the development of novel nodal staging system. The overall survival (OS) of patients with pararespiratory LNM only (Group A), paradigestive LNM only (Group B), and both sites (Group C) was evaluated by Kaplan-Meier. Cox proportional hazards models were used to identify the independent prognostic factors. An nN staging system considering both the LNM site and count was developed and evaluated by the area under the receiver operating characteristic curve (AUC).ResultsIn total, 1313 patients were included and split into training (n = 1033) and external validation (n = 280) cohorts. There were 342 (26.0%), 568 (43.3%) and 403 (30.7%) patients in groups A, B and C, respectively. The OS of patients with pararespiratory and patients with paradigestive LNM presented significant differences in the training and validation cohorts (P < 0.050). In the training cohort, LNM site was an independent prognostic factor (hazard ratio: 1.58, 95% confidence intervals: 1.41-1.77, P < 0.001). The nN staging definition: nN1 (1-2 positive pararespiratory/paradigestive LNs), nN2 (3-6 pararespiratory LNs or 1 pararespiratory with 1paradigestive LN), nN3 (3-6 LNs with ≥ 1 paradigestive LN), nN4 (≥ 7 LNs). Subsets of patients with different nN stages showed significant differences in OS (P < 0.050). The prognostic model of the nN staging system presented higher performance in the training and validation cohorts at 3-year OS (AUC, 0.725 and 0.751, respectively) and 5-year OS (AUC, 0.740 and 0.793, respectively) than the current N staging systems.ConclusionsCompared to pararespiratory LNM, the presence of paradigestive LNM is associated with worse OS. The nN staging system revealed superior prognostic ability than current N staging systems.

Project description:PurposeTo investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC).Methods and materialsTwo hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models.ResultsSixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05).ConclusionRadiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT-based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients.

Dataset Information

A new classification of lymph node metastases according to the lymph node stations for predicting prognosis in surgical patients with esophageal squamous cell carcinoma.

Publications

A new classification of lymph node metastases according to the lymph node stations for predicting prognosis in surgical patients with esophageal squamous cell carcinoma.

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