Nomogram to predict lymph node metastasis in patients with early oesophageal squamous cell carcinoma

Zheng, Hao; Tang, Han; Wang, H; Fang, Yuan; Shen, Yaxing; Feng, Mingxiang; Xu, Songtao; Fan, Hong; Wang, Q; Tan, Lijie

doi:10.1002/bjs.10882

Cited by 30 publications

(48 citation statements)

References 33 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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“…We hypothesized a nomogram could be constructed by combining selected clinical and pathological variables using a multivariate model to predict the likelihood of postoperative liver metastasis in early breast cancer patients. This nomogram can be used to identify subgroups of high-risk patients, develop targeted screening and new preventive treatment strategies for early-stage breast cancer patients, and even improve life quality and survival outcomes [15,16].…”

Section: Introductionmentioning

confidence: 99%

Predictive nomogram of subsequent liver metastasis after mastectomy or breast-conserving surgery in patients with nonmetastatic breast cancer

Yang

Xiao

Zheng

et al. 2020

Preprint

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BACKGROUND Metastasis accounts for the majority of deaths in patients with breast cancer. Liver metastasis is reported common to breast cancer patients. The purpose of this study was to construct a nomogram to predict the likelihood of subsequent liver metastasis in patients with nonmetastatic breast cancer, thus high-risk patient populations can be prevented and monitored. METHODS A total of 1840 patients with stage I-III breast cancer were retrospectively included and analyzed. A nomogram was constructed to predict liver metastasis based on multivariate logistic regression analysis. C-index, calibration curve and decision curve analysis were used to evaluate the predictive performance of the model. RESULTS The nomogram included three variables related to liver metastasis: HER2 status (odds ratio (OR) 1.86, 95%CI 1.02 to 3.41; P =0.045), tumor size (OR 3.62, 1.91 to 6.87; P < 0.001) and lymph node metastasis (OR 2.26, 1.18 to 4.34; P =0.014). The C index in the training cohort was 0.699 and it was 0.814 in the validation cohort (691 patients). The nomogram was well-calibrated, with no statistical difference between the predicted and the observed probabilities. CONCLUSION We have developed and validated a robust tool enabled to predict subsequent liver metastasis in patients with nonmetastatic breast cancer. Distinguishing a population of patients at high risk of liver metastasis will facilitate preventive treatment or monitoring of liver metastasis.

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“…Among the published prediction models for LN metastasis, there are already three LR-based nomograms. [12][13][14] However, there are still no established ANN models. The aim of this study was to establish an ANN model and assess its ability by comparing it with a traditional Logistic Regression (LR) model for predicting LN metastasis in patients with SESCC.…”

Section: Introductionmentioning

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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Nomogram to predict lymph node metastasis in patients with early oesophageal squamous cell carcinoma

Abstract: A validated nomogram for patients with early oesophageal SCC can predict the risk of lymph node metastasis.

Cited by 30 publications

References 33 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

Predictive nomogram of subsequent liver metastasis after mastectomy or breast-conserving surgery in patients with nonmetastatic breast cancer

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

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