Evidence map›Paper›PMID 34702197›Full record

ArticleBMC cancer2021

Nomogram to predict overall survival based on the log odds of positive lymph nodes for patients with endometrial carcinosarcoma after surgery.

Linzhi Gao, Jun Lyu, Xiaoya Luo, Dong Zhang, Guifang Jiang, Xian Zhang, Xuesong Gao, Shaolie Zheng, Xiaoyu Wang, Yuan Shen

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 20% 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

7 citing papers in PubMed, 8 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

10 authors at 2 institutions in 1 country.

Linzhi Gao *Department of Gynaecology, The First Affiliated Hospital of Jinan University, 613 Whampoa Avenue, Tianhe District, Guangzhou, 510632, China.
Jun Lyu *Department of Clinical Research, The First Affiliated Hospital of Jinan University, 613 Whampoa Avenue, Tianhe District, Guangzhou, China.
Xiaoya Luo *Department of Gynaecology, The First Affiliated Hospital of Jinan University, 613 Whampoa Avenue, Tianhe District, Guangzhou, 510632, China.
Dong ZhangDepartment of Gynaecology, The First Affiliated Hospital of Jinan University, 613 Whampoa Avenue, Tianhe District, Guangzhou, 510632, China.
Guifang JiangDepartment of Gynecology and Obstetrics, The Affiliated Shunde Hospital of Jinan University, 50 East Guizhou Avenue, Shunde District, Foshan, China.
Xian ZhangDepartment of Gynaecology, The First Affiliated Hospital of Jinan University, 613 Whampoa Avenue, Tianhe District, Guangzhou, 510632, China.
Xuesong GaoDepartment of Gynaecology, The First Affiliated Hospital of Jinan University, 613 Whampoa Avenue, Tianhe District, Guangzhou, 510632, China.
Shaolie ZhengDepartment of Gynaecology, The First Affiliated Hospital of Jinan University, 613 Whampoa Avenue, Tianhe District, Guangzhou, 510632, China.
Xiaoyu WangDepartment of Gynaecology, The First Affiliated Hospital of Jinan University, 613 Whampoa Avenue, Tianhe District, Guangzhou, 510632, China. twxy163@163.com.
Yuan ShenDepartment of Gynaecology, The First Affiliated Hospital of Jinan University, 613 Whampoa Avenue, Tianhe District, Guangzhou, 510632, China. shenyuan_jnu@163.com.
First Affiliated Hospital of Jinan University · CNThe Affiliated Shunde Hospital of Jinan University

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAims to compare the prognostic performance of the number of positive lymph nodes (PLNN), lymph node ratio (LNR) and log odds of metastatic lymph nodes (LODDS) and establish a prognostic nomogram to predict overall survival (OS) rate for patients with endometrial carcinosarcoma (ECS).

methodsPatients were retrospectively obtained from Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2015. The prognostic value of PLNN, LNR and LODDS were assessed. A prediction model for OS was established based on univariate and multivariate analysis of clinical and demographic characteristics of ECS patients. The clinical practical usefulness of the prediction model was valued by decision curve analysis (DCA) through quantifying its net benefits.

resultsThe OS prediction accuracy of LODDS for ECS is better than that of PLNN and LNR. Five factors, age, tumor size, 2009 FIGO, LODDS and peritoneal cytology, were independent prognostic factors of OS. The C-index of the nomogram was 0.743 in the training cohort. The AUCs were 0.740, 0.682 and 0.660 for predicting 1-, 3- and 5-year OS, respectively. The calibration plots and DCA showed good clinical applicability of the nomogram, which is better than 2009 FIGO staging system. These results were verified in the validation cohort. A risk classification system was built that could classify ECS patients into three risk groups. The Kaplan-Meier curves showed that OS in the different groups was accurately differentiated by the risk classification system and performed much better than FIGO 2009.

conclusionOur results indicated that LODDS was an independent prognostic indicator for ECS patients, with better predictive efficiency than PLNN and LNR. A novel prognostic nomogram for predicting the OS rate of ECS patients was established based on the population in the SEER database. Our nomogram based on LODDS has a more accurate and convenient value for predicting the OS of ECS patients than the FIGO staging system alone.

Indexed as

AdultAgedAged, 80 and overCarcinosarcomaEndometrial NeoplasmsFemaleHumansLymph NodesMiddle AgedNomogramsPrognosisRisk FactorsSurvival RateYoung AdultEndometrial carcinosarcomaLog odds of positive lymph nodesNomogramOverall survival rateSEER

Identifiers

PMID34702197
PMCPMC8549209
OpenAlexW3208611566

What OpenQuestion holds

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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.