Evidence map›Paper›PMID 35756674›Full record

ArticleFrontiers in oncology2022

Integration of Inflammation-Immune Factors to Build Prognostic Model Predictive of Prognosis and Minimal Residual Disease for Hepatocellular Carcinoma.

Xin Xu, Ao Huang, De-Zhen Guo, Yu-Peng Wang, Shi-Yu Zhang, Jia-Yan Yan, Xin-Yu Wang, Ya Cao, Jia Fan, Jian Zhou and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
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 at 3 institutions in 1 country.

Xin XuLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
Ao HuangLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
De-Zhen GuoLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
Yu-Peng WangLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
Shi-Yu ZhangLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
Jia-Yan YanLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
Xin-Yu WangLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
Ya CaoCancer Research Institute, Xiangya School of Medicine, Central South University; Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Xiangya Hospital, Central South University, Changsha, China.
Jia FanLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
Jian ZhouLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
Xiu-Tao FuLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
Ying-Hong ShiLiver Cancer Institute, Zhongshan Hospital, Fudan University and Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai, China.
Fudan University · CNZhongshan Hospital · CNCentral South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tumor recurrence after hepatectomy is high for hepatocellular carcinoma (HCC), and minimal residual disease (MRD) could be the underlying mechanism. A predictive model for recurrence and presence of MRD is needed. Methods: Common inflammation-immune factors were reviewed and selected to construct novel models. The model consisting of preoperative aspartate aminotransferase, C-reactive protein, and lymphocyte count, named ACLR, was selected and evaluated for clinical significance. Results: Among the nine novel inflammation-immune models, ACLR showed the highest accuracy for overall survival (OS) and time to recurrence (TTR). At the optimal cutoff value of 80, patients with high ACLR (> 80) had larger tumor size, higher Edmondson's grade, more vascular invasion, advanced tumor stage, and poorer survival than those with low ACLR (≤ 80) in the training cohort (5-year OS: 43.3% vs. 80.1%, Conclusions: The novel inflammation-immune model, ACLR, could effectively predict prognosis, and the presence of MRD before hepatectomy and might guide the decision on resection margin for patients with HCC.

Indexed as

hepatocellular carcinomaimmunityinflammationprognosisprognostic model

Identifiers

PMID35756674
PMCPMC9213691
OpenAlexW4281782337

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.