Evidence map›Paper›PMID 33896819›Full record

ArticleCancer biomarkers : section A of Disease markers2021

Prognostic significance of preoperative systemic immune-inflammation index in combined hepatocellular-cholangiocarcinoma.

Feng Zhang, Ke-Shu Hu, Shen-Xin Lu, Miao Li, Rong-Xin Chen, Zheng-Gang Ren, Ying-Hong Shi, Xin Yin

Abstract read
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Article in Cancer biomarkers : section A of Disease markers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

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0cells of the map it votes in
5citing 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.

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2 · The registry

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3 · Its place in the literature

Who cites it

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

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4 · The record

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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

8 authors.

Feng ZhangLiver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.
Ke-Shu HuLiver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.
Shen-Xin LuLiver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.
Miao LiLiver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.
Rong-Xin ChenLiver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.
Zheng-Gang RenLiver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.
Ying-Hong ShiLiver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.
Xin YinLiver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInflammation-based prognostic scores have been increasingly used for prognosis prediction in malignant tumors. However, no existing study has comprehensively evaluated these scores in combined hepatocellular-cholangiocarcinoma (cHCC-CCA).

objectiveThis study aimed to identify a robust inflammation-based prognostic predictor for cHCC-CCA.

methodsWe retrospectively analyzed 220 patients pathologically confirmed as Allen type C cHCC-CCA. The univariate and multivariate analyses were used to explore the associations between clinical variables and prognosis of cHCC-CCA. The propensity score-matching (PSM) was performed to reduce the effects of potential cofounders and selection bias. Finally, the predictive values of different inflammation-based indices were compared by using time-dependent receiver operating characteristic (ROC) curves.

resultsThe systemic immune-inflammation index (SII) and aspartate aminotransferase to platelet ratio index (APRI) were identified as independent prognostic predictors in multivariate analysis. After PSM, the survival differences were still significant between SII-high group and SII-low group (P= 0.016 for RFS and P= 0.001 for OS). Further ROC analysis showed that the SII harbored the largest 1-, 3- and 5-year area under the curves (AUC) values as compared with other scores.

conclusionsThe SII may serve as a preferable predictor of both recurrence-free survival (RFS) and overall survival (OS) in patients with cHCC-CCA.

Indexed as

Carcinoma, HepatocellularCholangiocarcinomaFemaleHumansInflammationLiver NeoplasmsMaleMiddle AgedPrognosisSurvival AnalysisCombined hepatocellular-cholangiocarcinomaoverall survivalpropensity score-matchingrecurrence-free survivalsystemic immune-inflammation index

Identifiers

PMID33896819
PMCPMC12500008

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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.