Evidence map›Paper›PMID 36600994›Full record

ArticleJournal of inflammation research2022

A Systemic Inflammation Response Score for Prognostic Prediction in Hepatocellular Carcinoma Patients After Hepatectomy.

Deyao Zhang, Lanqing Huo, Yangxun Pan, Zhenyun Yang, Huilan Zeng, Xin Wang, Jinbin Chen, Juncheng Wang, Yaojun Zhang, Zhongguo Zhou and 2 more

Abstract read
In one paragraph

Article in Journal of inflammation research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
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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

12 authors.

Deyao Zhang *Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.ORCID 0000-0002-9654-8905
Lanqing Huo *Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
Yangxun Pan *Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.ORCID 0000-0002-3887-5852
Zhenyun Yang *Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
Huilan ZengCollaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
Xin WangCollaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
Jinbin ChenCollaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.ORCID 0000-0002-6619-1247
Juncheng WangCollaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
Yaojun ZhangCollaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.ORCID 0000-0002-9752-4729
Zhongguo ZhouCollaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
Minshan ChenCollaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
Dandan HuCollaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To investigate the value of preoperative systemic inflammation response (SIRS) score in predicting the prognosis of hepatocellular carcinoma (HCC) after hepatectomy. Patients and Methods: The study analyzed 1001 patients with pathologically proven HCC who received curative resection at Sun Yat-sen University Cancer Center between March 2016 and May 2020. Patients were randomly divided into a training cohort (n = 751) and a validation cohort (n = 250). Clinicopathological characteristics were collected retrospectively. The SIRS score formula was based on the results of a multivariate cox analysis of hematological inflammation indexes in the training cohort. Then, a nomogram consisting of the SIRS score was constructed and the calibration plot, areas under the receiver operating characteristic (AUC) curve, and decision curve analysis (DCA) showed good predictive ability. Results: Univariate and multivariate cox analysis revealed that the SIRS score is an independent prognostic factor for OS in HCC patients. A higher SIRS score was associated with a larger maximum lesion diameter, poor tumor differentiation, a greater possibility of vascular invasion, and a more advanced cancer stage. When the nomogram was used to predict 1-year, 3-year, and 5-year survival rates, the AUC in the training cohort was 0.763, 0.712, and 0.687, respectively; In the validation cohort, it was 0.715, 0.648, and 0.614, respectively. The AUC of this nomogram showed significantly better predictive performance than those of commonly used staging systems. Conclusion: The preoperative SIRS score has good efficacy in predicting the prognosis of HCC patients undergoing hepatectomy, and nomograms based on the SIRS score can potentially guide individualized follow-up and adjuvant therapy.

Indexed as

HCChepatocellular carcinomaliver cancerNomogrampreoperativeprognosissystemic inflammation response score

Identifiers

PMID36600994
PMCPMC9807220

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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.