Evidence map›Paper›PMID 28705205›Full record

Trial reportBMC endocrine disorders2017

Clinicopathological features and prognosis of gastroenteropancreatic neuroendocrine neoplasms in a Chinese population: a large, retrospective single-centre study.

Meng Zhang, Ping Zhao, Xiaodan Shi, Ahong Zhao, Lianfeng Zhang, Lin Zhou

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in BMC endocrine disorders, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 9% 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.

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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.

  1. Pooled it
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  13. Neuroendocrine neoplasia and bone (Review).Experimental and therapeutic medicine · 2021
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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

6 authors at 1 institution in 1 country.

Meng ZhangDepartment of Gastroenterology, the First Affiliated Hospital of Zhengzhou University, No.1, East Jianshe Road, Zhengzhou, 450052, China.
Ping ZhaoDepartment of Gastroenterology, the First Affiliated Hospital of Zhengzhou University, No.1, East Jianshe Road, Zhengzhou, 450052, China.
Xiaodan ShiDepartment of Gastroenterology, the First Affiliated Hospital of Zhengzhou University, No.1, East Jianshe Road, Zhengzhou, 450052, China.
Ahong ZhaoDepartment of Pathology, the First Affiliated Hospital of Zhengzhou University, No.1, East Jianshe Road, Zhengzhou, 450052, China.
Lianfeng ZhangDepartment of Gastroenterology, the First Affiliated Hospital of Zhengzhou University, No.1, East Jianshe Road, Zhengzhou, 450052, China.
Lin ZhouDepartment of Gastroenterology, the First Affiliated Hospital of Zhengzhou University, No.1, East Jianshe Road, Zhengzhou, 450052, China. ZL372@126.com.
First Affiliated Hospital of Zhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are the most common type of neuroendocrine tumors, accounting for more than half of neuroendocrine neoplasms (NENs). We performed a retrospective study in our center to investigate the clinicopathological features, risk factors of metastasis, and prognosis of GEP-NENs in a Chinese population.

methodsFour hundred forty patients with GEP-NENs treated at the First Affiliated Hospital of Zhengzhou University between January 2011 and March 2016 were analyzed retrospectively. Multivariate logistic regression was performed to identify independent risk factors for metastasis of the tumors. The Kaplan-Meier method was used for survival analysis, and log-rank tests for comparisons among groups.

resultsPrimary sites were the stomach (24.3%), rectum (24.1%), pancreas (20.5%), esophagus (12.3%), unknown primary origin (UPO-NEN) (8.0%), duodenum (6.1%). Three hundred eighty-nine of the 440 GEP-NENs cases (88.4%) were non-functional tumors, and patients had non-specific symptoms, which could have led to delay in diagnosis and treatment. Neuroendocrine tumor, neuroendocrine carcinoma, and mixed adenoendocrine carcinoma were 56.8%, 33.2% and 3.2%, respectively, of the cases. One hundred thirty (29.5%) of the tumors were G1, 120 (27.3%) G2, and 190 (43.2%) G3. The immunohistochemical positive rate of synaptophysin was 97.7% and of chromogranin 48.7%. Logistic regression analysis revealed that the diameter and pathological classification of tumors were the most important predictors for metastasis. The median survival time was 34 months for patients with well-differentiated neuroendocrine tumors grade G3 and 11 months for poorly differentiated neuroendocrine carcinoma. The median survival time of patients with localized disease, regional disease, and distant disease was 36 months, 15 month, and 6 months, respectively.

conclusionsThis study constitutes a comprehensive analysis of the clinicopathological features of GEP-NENs in a Chinese population. GEP-NENs may occur at any part of the digestive system. The diameter and pathological classification of tumor are the most important predictors for metastasis. The prognosis is poor for patients with poorly differentiated neuroendocrine cancers and distant metastases.

Indexed as

ChinaCombined Modality TherapyFemaleFollow-Up StudiesHumansIntestinal NeoplasmsMaleMiddle AgedMultimodal ImagingNeoplasm MetastasisNeuroendocrine TumorsPancreatic NeoplasmsPrevalencePrognosisRetrospective StudiesStomach NeoplasmsCarcinoid tumorGastrinomaGastro enteropancreatic neuroendocrine tumorsIslet cell tumorNeuroendocrine cancersNeuroendocrine neoplasmsNeuroendocrine tumors

Identifiers

PMID28705205
PMCPMC5508659
OpenAlexW2734702374

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.