Evidence map›Paper›PMID 30174862›Full record

ArticleJournal of thoracic disease2018

A nomogram for predicting lymph node metastasis in surgically resected T1 esophageal squamous cell carcinoma.

Weiyu Shen, Yaxing Shen, Lijie Tan, Chenghua Jin, Yong Xi

Registry-linked trialOpen access · hybridAbstract read
In one paragraph

Article in Journal of thoracic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06256185 (Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma), which is not on this map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06256185 nacompletednot on this map

Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma: A Multicenter Study

TypeinterventionalSponsorShanghai Zhongshan HospitalRan2010 to 2023Enrolled1,267ConditionsLymph Node MetastasisArmsesophagectomy
3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Weiyu ShenDepartment of Thoracic Surgery, Ningbo Medical Center Lihuili Eastern Hospital, Ningbo 315040, China.
Yaxing ShenDepartment of Thoracic Surgery, Zhongshan Hospital of Fudan University, Shanghai 200032, China.
Lijie TanDepartment of Thoracic Surgery, Zhongshan Hospital of Fudan University, Shanghai 200032, China.
Chenghua JinDepartment of Thoracic Surgery, Ningbo Medical Center Lihuili Eastern Hospital, Ningbo 315040, China.
Yong XiDepartment of Thoracic Surgery, Ningbo Medical Center Lihuili Eastern Hospital, Ningbo 315040, China.
Ningbo Medical Center Lihuili Hospital · CNSun Yat-sen University · CNZhongshan Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEndoscopic therapies for T1 esophageal carcinoma have been increasingly used around the world. However, the procedures are limited by without lymph nodes harvested. The risk of lymph node metastasis (LNM) should been established. Our objective was to construct a nomogram model for predict risks of LNM in patients with pT1 esophageal squamous cell carcinoma (ESCC).

methodsWe reviewed the records of 221 patients with pT1 ESCC who underwent surgical resection and radical lymphadenectomy. Clinicopathological variables were analyzed univariate and multivariate logistic regression analysis. A nomogram for predicting risk of LNM was constructed and validated using bootstrap resampling.

resultsOf the 221 patients, 53 patients had been examined as LNM. Following multivariate analysis, poor differentiation (P=0.0006), lymphovascular invasion (P<0.0001) and SM3 (tumor invades the lower third of the submucosal layer) (P=0.0192) cancer were significantly independent risk factors for LNM and were entered into the nomogram. The nomogram showed a robust discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.8667. The calibration curves for the probability of LNM showed optimal agreement between the probability as predicted by the nomogram and the actual probability.

conclusionsWe established a nomogram that can provide individual predicting for LNM in T1 ESCC, and this model has the potential clinical utility in making therapeutic procedures.

Indexed as

esophageal squamous cell carcinoma (ESCC)nomogramT1

Identifiers

PMID30174862
PMCPMC6106027
OpenAlexW2883664804

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.