Evidence map›Paper›PMID 33544202›Full record

ArticleArchives of gynecology and obstetrics2021

Uterine corpus invasion in cervical cancer: a multicenter retrospective case-control study.

Weili Li, Fangjie He, Ping Liu, Hui Duan, Yan Ni, Shaoguang Wang, Lihong Lin, Zhaohong Yin, Xiaolin Chen, Lu Yin and 4 more

Abstract read
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In one paragraph

Article in Archives of gynecology and obstetrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

5 citing papers in PubMed, 10 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

14 authors at 6 institutions in 1 country.

Weili Li *Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Fangjie He *Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Ping Liu *Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Hui Duan *Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Yan NiDepartment of Obstetrics and Gynecology, Yuncheng Central Hospital, Yuncheng, 044000, China.
Shaoguang WangDepartment of Obstetrics and Gynecology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000, China.
Lihong LinDepartment of Gynecologic Oncology, Anyang Cancer Hospital, Anyang, 455000, China.
Zhaohong YinDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Xiaolin ChenDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Lu YinDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Lixia WangDepartment of Pathology, Shanxi Cancer Hospital, Taiyuan, 030013, China.
Yueping LiuDepartment of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
Zhonghua LuanDepartment of Pathology, Yuncheng Central Hospital, Yuncheng, 044000, China.
Chunlin ChenDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China. ccl1@smu.edu.cn.ORCID http://orcid.org/0000-0002-1708-3047
Nanfang Hospital · CNYuncheng University · CNAnyang Tumor Hospital · CNHebei Medical University · CNQingdao University · CNShanxi Provincial Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo determine the accuracy of uterine corpus invasion (UCI) diagnosis in patients with cervical cancer and identity risk factors for UCI and depth of invasion.

methodsClinical data of patients with cervical cancer who underwent hysterectomy between 2004 and 2016 were retrospectively reviewed. UCI was assessed on uterine pathology. Independent risk factors for UCI and depth of invasion were identified using binary and ordinal logistic regression models, respectively.

resultsA total of 2,212 patients with cervical cancer from 11 medical institutions in China were included in this study. Of these, 497 patients had cervical cancer and UCI, and 1,715 patients had cervical cancer and no UCI, according to the original pathology reports. Retrospective review of the original pathology reports revealed a missed diagnosis of UCI in 54 (10.5%) patients and a misdiagnosis in 36 (2.1%) patients. Therefore, 515 patients with cervical cancer and UCI (160 patients with endometrial invasion, 176 patients with myometrial invasion < 50%, and 179 patients with myometrial invasion ≥ 50%), and 1697 patients with cervical cancer without UCI were included in the analysis. Older age, advanced stage, tumor size, adenocarcinoma, parametrial involvement, resection margin involvement, and lymph node metastasis were independent risk factors for UCI. These risk factors, except resection margin involvement, were independently associated with depth of UCI.

conclusionsUCI may be missed or misdiagnosed in patients with cervical cancer on postoperative pathological examination. Older age, advanced stage, tumor size, adenocarcinoma, parametrial involvement, resection margin involvement, and lymph node metastasis were independent risk factors for UCI and depth of UCI, with the exception of resection margin involvement.

Indexed as

HysterectomyAdenocarcinomaAdultAgedCase-Control StudiesChinaFemaleHumansLymphatic MetastasisMiddle AgedNeoplasm InvasivenessNeoplasm StagingRetrospective StudiesRisk FactorsUterine Cervical NeoplasmsCervical cancerDiagnostic accuracyInvasion depthPathological reviewUterine corpus

Identifiers

PMID33544202
OpenAlexW3127466836

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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.