Evidence map›Paper›PMID 30259688›Full record

ArticleCancer medicine2018

A novel inflammation-based nomogram system to predict survival of patients with hepatocellular carcinoma.

Jinbin Chen, Aiping Fang, Minshan Chen, Yiminjiang Tuoheti, Zhongguo Zhou, Li Xu, Jiancong Chen, Yangxun Pan, Juncheng Wang, Huilian Zhu and 1 more

Abstract read
In one paragraph

Article in Cancer medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

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0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
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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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3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Jinbin ChenState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China.ORCID 0000-0002-6619-1247
Aiping FangDepartment of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Minshan ChenState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China.
Yiminjiang TuohetiState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China.
Zhongguo ZhouState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China.ORCID 0000-0002-1929-4278
Li XuState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China.
Jiancong ChenState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China.
Yangxun PanState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China.
Juncheng WangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China.
Huilian ZhuDepartment of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Yaojun ZhangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China.ORCID 0000-0002-9752-4729

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimThe existed staging systems were limited in the accuracy of prediction for overall survival (OS) of hepatocellular carcinoma (HCC) patients. The aim of this study is to establish a novel inflammation-based prognostic system with nomogram for HCC patients.

methodsA prospective cohort of patients was recruited and assigned to the training cohort (n = 659) and validation cohort (n = 320) randomly. Different inflammation-based score systems were evaluated to select the best one predicting overall survival (OS). The inflammation-based score system with the highest predicting value and the parameters best reflecting tumor burden identified by multivariate analysis were selected to construct a novel predicting nomogram system. The predictive accuracy and discriminative ability of the nomogram were evaluated by concordance index (C-index) and calibration curve and compared with conventional staging systems.

resultsWith a highest C-index and areas under the receiver operating characteristic curve (AUC), C-reactive protein/albumin ratio (CAR) was selected to construct the novel system, along with tumor number, tumor size, macrovascular invasion and extra-hepatic metastases. The C-index of the nomogram was 0.813 (95% CI, 0.789-0.837) in the training cohort and 0.794 (95% CI, 0.756-0.832) in the validation cohort. The calibration curve for predicting probability of survival showed that the nomogram had a high consistency with follow-up data. The C-index of the novel system was higher than other conventional staging systems (P < 0.001).

conclusionsThe novel inflammation-based nomogram, developed from prospectively collected data in the present study, predicted the OS of HCC patients.

Indexed as

NomogramsAlbuminsBiomarkers, TumorCarcinoma, HepatocellularC-Reactive ProteinFemaleHumansLiver NeoplasmsMalePrognosisProspective StudiesRandom AllocationTumor BurdenAlbuminsBiomarkers, TumorC-Reactive Proteinhepatocellular carcinomainflammation-based score systemnomogramprognostic value

Identifiers

PMID30259688
PMCPMC6198220

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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.