Evidence map›Paper›PMID 39493259›Full record

ReviewTherapeutic advances in gastroenterology2024

An overview of risk assessment and monitoring of malignant transformation in cirrhotic nodules.

Zhun Xiao, Fangming Yang, Zheng Liu, Xinju Chen, Suping Ma, Heng Li

Abstract readReview
In one paragraph

Review in Therapeutic advances in gastroenterology, 2024. 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
–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

1 citing paper in PubMed.

  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

6 authors.

Zhun XiaoDepartment of Digestive Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.ORCID https://orcid.org/0000-0003-3420-6915
Fangming YangDepartment of Digestive Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.
Zheng LiuDepartment of Combination of Traditional Chinese Medicine and Western Medicine, Medical College, Henan University of Chinese Medicine, Zhengzhou, Henan, China.
Xinju ChenDepartment of Digestive Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.
Suping MaDepartment of Digestive Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, No. 19 Renmin Road, Zhengzhou 450000, China.
Heng LiYong Loo Lin School of Medicine, National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore.ORCID https://orcid.org/0000-0003-4997-671X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cirrhotic liver nodules can progress to hepatocellular carcinoma (HCC) through a multi-step carcinogenesis model, with dysplastic nodules being particularly high risk. Currently, monitoring the progression of non-HCC cirrhotic nodules is primarily through dynamic observation, but there is a lack of sensitive, efficient, and convenient methods. Dynamic monitoring and risk evaluation of malignant transformation are essential for timely treatment and improved patient survival rates. Routine liver biopsies are impractical for monitoring, and imaging techniques like ultrasound, computed tomography, and magnetic resonance imaging are not suitable for all patients or for accurately assessing subcentimeter nodules. Identifying serum biomarkers with high sensitivity, specificity, and stability, and developing a multi-index evaluation model, may provide a more convenient and efficient approach to monitoring pathological changes in cirrhotic nodules.

Indexed as

cirrhotic nodulehepatocellular carcinomaliver biopsyrisk factors

Identifiers

PMID39493259
PMCPMC11528798

What OpenQuestion holds

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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.