Evidence map›Paper›PMID 36733675›Full record

ArticleFrontiers in surgery2022

A population-based investigation: How to identify high-risk T1-2N0 esophageal cancer patients?

Peng Luo, Jie Wu, Xiankai Chen, Yafan Yang, Ruixiang Zhang, Xiuzhu Qi, Yin Li

Abstract read
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In one paragraph

Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Peng LuoDepartment of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jie WuDepartment of Urology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiankai ChenDepartment of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yafan YangDepartment of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Ruixiang ZhangDepartment of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiuzhu QiDepartment of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.
Yin LiDepartment of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Newly diagnosed T1-2N0 esophageal cancer (EC) is generally deemed as early local disease, with distant metastases (DM) easily overlooked. This retrospective study aimed to describe the metastatic patterns, identify risk factors and established a risk prediction model for DM in T1-2N0 EC patients. Methods: A total of 4623 T1-2N0 EC patients were identified in the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2018. Multivariable logistic regression was used to identify risk factors for DM. A nomogram was developed for presentation of the final model. Results: Of 4623 T1-2N0 patients, 4062 (87.9%) had M0 disease and 561 (12.1%) had M1 disease. The most common metastatic site was liver ( Conclusions: We identified independent predictive factors for DM, as well as for BoM, BrM, LiM and LuM. Above all, a practical and convenient nomogram with a great accuracy to predict DM probability for T1-2N0 EC patients was established.

Indexed as

distant metastasesnomogramrisk factorsSEERT1-2N0 esophageal cancer

Identifiers

PMID36733675
PMCPMC9888256

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