Predicting lymph node metastasis for endoscopic resection of superficial esophageal squamous cell carcinoma

Won, Dae; Jung, Da Hyun; Kim, Joo Hang; Park, Sung Chul; Youn, Young Hoon; Park, Hyojin

doi:10.1016/j.jtcvs.2018.07.034

Cited by 27 publications

(40 citation statements)

References 24 publications

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“…To address this issue, tumor invasion depth was reviewed in detail and stratified into four layers including the muscularis mucosa or SM1 layer in the present study. Compared with previous studies, this may strengthen the clinical value and usefulness of our nomogram as a decision‐making tool for patients undergoing endoscopic resection for superficial ESCC …”

Section: Discussionsupporting

confidence: 58%

“…Several studies have proposed predictive models for LN metastasis in patients with superficial ESCC . These previous studies were limited in that they did not consider the sub‐layers of the mucosa and submucosa in the models despite the large differences in LN metastasis rates between ESCCs confined to the lamina propria and those invading the muscularis mucosa or superficial submucosal layer .…”

Section: Discussionmentioning

confidence: 99%

“…The risk of LN metastasis according to tumor invasion depth was analyzed in the context of mucosa versus submucosa only, and sub‐layers of the mucosa and submucosa such as the lamina propria, muscularis mucosa, and superficial submucosa layer were not considered in the analyses. This might limit the application of these predictive models in clinical practice when deciding the need for additional esophagectomy after endoscopic resection for superficial ESCC …”

Section: Introductionmentioning

confidence: 99%

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Nomogram for prediction of lymph node metastasis in patients with superficial esophageal squamous cell carcinoma

Min

Yang

Baek

et al. 2019

J of Gastro and Hepatol

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Background and Aim Knowledge of lymph node metastasis (LNM) status is crucial to determine whether patients with superficial esophageal squamous cell carcinoma (ESCC) can be cured with endoscopic resection alone, without the need for additional esophagectomy. The present study aimed to identify predictive factors and develop a prediction model for LNM in patients with superficial ESCC. Methods Clinicopathologic data from 501 patients with superficial ESCC treated with radical esophagectomy were reviewed. Stepwise logistic regression analysis determined the predictors of LNM. Using these predictors, a nomogram for predicting the risk of LNM was constructed and internally validated using a bootstrap resampling method. Results LNM rates of tumors invading the lamina propria, muscularis mucosa, and SM1 layers were 3.7%, 15.5%, and 40.7%, respectively. Deep tumor invasion depth, moderately or poorly differentiated histology, and lymphovascular invasion were independent predictors of LNM. ESCC with muscularis mucosa and SM1 invasion had odds ratios of 3.635 and 11.834, respectively, compared with that for ESCC confined to the lamina propria. Large tumor size (>2.0 cm) and presence of tumor budding showed borderline significance for LNM prediction. These five variables were incorporated into a nomogram. A constructed nomogram showed good calibration and good discrimination with an area under the receiver‐operating characteristic curve (area under the curve [AUC]) of 0.812. After bootstrapping, AUC was 0.811. Conclusions We developed a nomogram that can facilitate individualized prediction of risk of LNM in patients with superficial ESCC. This model can aid in decision‐making for the need for additional esophagectomy after endoscopic resection for superficial ESCC.

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Section: Discussionsupporting

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nomogram for prediction of lymph node metastasis in patients with superficial esophageal squamous cell carcinoma

Min

Yang

Baek

et al. 2019

J of Gastro and Hepatol

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“…Previous studies have identi ed clinicopathologic factors including T stage, G stage and tumor location, as predictors of the risk of LNM in patients with ESCC (14,(21)(22)(23)(24)(25). And some studies also aimed to construct LNM prediction models based on the aforementioned clinical variables (26,27). Ultimately, the nomogram consisting of 7 variables including 4 DEGs, T stage, G stage and tumor location was established to predict the probability of LNM (Fig.…”

Section: Construction and Validation Of A Gene-panel-based Nomogram Fmentioning

confidence: 99%

Development and Validation of a Gene-panel-based Nomogram for Prediction of Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma

Chen

Wang

et al. 2020

Preprint

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Background: Esophageal squamous cell carcinoma (ESCC) is one of the main histological subtypes of esophageal cancer. This study aimed to develop a gene-panel-based nomogram for identification of lymph node metastasis (LNM) in ESCC patients.Methods: RNA sequencing profiles of ESCC patients were obtained from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. A bioinformatic approach was employed to investigate differentially expressed genes (DEGs) between ESCC patients with LNM and those without. A 4-DEGs panel was eventually identified and integrated with clinical characteristics to construct a nomogram for predicting LNM. Predictive performance of the nomogram was further evaluated by calibration curves and concordance index (C-index). Results: A total of 179 ESCC patients with 32059 genes from the GEO dataset were included. Among these genes, 3524 DEGs were correlated with lymph node involvement. Meanwhile, TCGA dataset containing 93 ESCC patients was obtained, in which 82 DEGs were selected out of 18416 genes. Among the 11 communal DEGs, four genes were identified to be (ALG3, CPOX, LMLN, PSMD2) associated with LNM. A nomogram was established by integrating the four-gene panel and three clinical characteristics including T stage, G stage and tumor location. The nomogram exhibited good performance with C-indices of 0.710 and 0.693 in the GEO and TCGA datasets, respectively. Conclusion: Our novel 4-gene-based nomogram displayed its value in prediction of LNM in ESCC patients, which may be helpful in determining treatment approach for early-stage ESCC patients.

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“…SESCC. The model was developed with the largest sampling pool that we know of: the number of patients was twice greater than the previously published model data, 19,20 and a larger sample size generally leads to increased precision of the model.…”

mentioning

confidence: 99%

<p>Prediction of Lymph Node Metastasis in Superficial Esophageal Cancer Using a Pattern Recognition Neural Network</p>

Chen¹,

Zhou²,

Tang³

et al. 2020

CMAR

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Background or Purpose: It is important to predict nodal metastases in patients with early esophageal cancer to stratify patients for endoscopic resection or esophagectomy. This study was to establish a novel artificial neural network (ANN) and assess its ability by comparing it with a traditional logistic regression (LR) model for predicting lymph node (LN) metastasis in patients with superficial esophageal squamous cell carcinoma (SESCC). Methods: A primary cohort was established, composed of 733 patients who underwent esophagectomy for SESCC from December 2012 to December 2019. The following steps were applied: (i) predictor selection; (ii) development of an ANN and a LR model, respectively; (iii) cross-validation; and (iv) evaluation of performance between the two models. The diagnostic assessment was performed with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: The established ANN model had 6 significant predictors: a past habit of alcohol taking, tumor size, submucosal invasion, histologic grade, lymph-vessel invasion, and preoperative CT result. The ANN model performed better than the LR model in specificity (91.20% vs 72.59%, p=0.006), PPV (56.49% vs 39.78%, p=0.020), accuracy (90.72% vs 74.49%, p<0.0001), C-index (91.5% vs 86.8%, p<0.001), and IDI (improved by 23.3%, p<0.001). There were no differences between these two models in sensitivity (87.06% vs 83.21%, p=0.764), NPV (98.17% vs 95.21%, p=0.627), and NRI (improved by −1.1%, p=0.824). Conclusion: This ANN model is superior to the LR model and may become a valuable tool for the prediction of LN metastasis in patients with SESCC.

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Predicting lymph node metastasis for endoscopic resection of superficial esophageal squamous cell carcinoma

Abstract: We developed a risk stratification system that should facilitate the identification of patients with a high or low risk of lymph node metastasis. This may aid the precise selection of patients who can undergo endoscopic resection.

Cited by 27 publications

References 24 publications

Nomogram for prediction of lymph node metastasis in patients with superficial esophageal squamous cell carcinoma

Nomogram for prediction of lymph node metastasis in patients with superficial esophageal squamous cell carcinoma

Development and Validation of a Gene-panel-based Nomogram for Prediction of Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma

<p>Prediction of Lymph Node Metastasis in Superficial Esophageal Cancer Using a Pattern Recognition Neural Network</p>

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