Evidence map›Paper›PMID 27147574›Full record

ArticleOncotarget2016

Combined preoperative concentrations of CEA, CA 19-9, and 72-4 for predicting outcomes in patients with gastric cancer after curative resection.

Xuechao Liu, Haibo Qiu, Jianjun Liu, Shangxiang Chen, Dazhi Xu, Wei Li, Youqing Zhan, Yuanfang Li, Yingbo Chen, Zhiwei Zhou and 1 more

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.8field-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

16 citing papers in PubMed, 23 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

11 authors at 3 institutions in 1 country.

Xuechao LiuSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Haibo QiuSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Jianjun LiuSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Shangxiang ChenSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Dazhi XuSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Wei LiSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Youqing ZhanSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Yuanfang LiSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Yingbo ChenSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Zhiwei ZhouSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Xiaowei SunSun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Sun Yat-sen University · CNState Key Laboratory of Oncology in South ChinaSun Yat-sen University Cancer Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In many cancers, prognostic factors are useful for identifying high-risk patients and in individualizing treatment. We sought to determine whether a combination of tumor markers (CTM) would improve prognostic accuracy in patients with gastric cancer (GC). The CTM score, which is derived from serum concentrations of carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA 19-9), and carbohydrate antigen 72-4 (CA 72-4), was tested retrospectively in 1134 patients with GC undergoing curative resection between October 2000 and December 2012. The CTM score was 2 for patients with two or three elevated markers, 1 for those with one elevated marker, and 0 for those no elevated markers. Overall survival (OS) in patients with CTM scores 0, 1, and 2 was 61.8%, 31.4%, and 15.1%, respectively (P<.001). The CTM score independently predicted OS on multivariate analysis (HR, 1.95; 95% CI, 1.73 to 2.21; P<.001). Moreover, the area under the receiver operating characteristics curve of the CTM score (0.67; 95% CI, 0.64 to 0.70) was higher than the values of any individual marker (0.63, 0.57, 0.57; P<.001 for all comparisons). The CTM score independently predicted postoperative survival in GC, and it may have better clinical utility than individual tumor markers for identifying high-risk patients with GC.

Indexed as

AdolescentAdultAgedAged, 80 and overAntigens, Tumor-Associated, CarbohydrateArea Under CurveBiomarkers, TumorCA-19-9 AntigenCarcinoembryonic AntigenFemaleGastrectomyHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisAntigens, Tumor-Associated, CarbohydrateBiomarkers, TumorCA-19-9 AntigenCA-72-4 antigenCarcinoembryonic Antigencombination of preoperative tumor markersCTMprognosisscoring systemtumor markers

Identifiers

PMID27147574
PMCPMC5085242
OpenAlexW2342615397

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.