Evidence map›Paper›PMID 40337459›Full record

ArticlePrecision radiation oncology2024

Radiation induced liver injury (RILI) evaluation using longitudinal computed tomography (CT) in image-guided precision murine radiotherapy.

Yuenan Wang, Fada Guan, Siyuan Wang, Wanwei Jian, Mohammad Khan

Abstract read
In one paragraph

Article in Precision radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Editorial: 2Precision radiation oncology · 2025
    Article
  2. Article
  3. Article
  4. Article
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

5 authors.

Yuenan WangDepartment of Therapeutic Radiology Yale University School of Medicine New Haven Connecticut USA.ORCID https://orcid.org/0000-0001-6280-6956
Fada GuanDepartment of Therapeutic Radiology Yale University School of Medicine New Haven Connecticut USA.ORCID https://orcid.org/0000-0001-8477-7391
Siyuan WangDepartment of Radiation Oncology Peking University Shenzhen Hospital Shenzhen P.R. China.
Wanwei JianDepartment of Radiation Therapy The Second Affiliated Hospital Guangzhou University of Chinese Medicine Guangzhou China.
Mohammad KhanDepartment of Radiation Oncology Winship Cancer Institute Emory University Atlanta Georgia USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: We aim to perform image-guided, dose-escalated, well-controlled liver-irradiated animal studies and subsequently evaluate radiation induced liver injury (RILI) using longitudinal CT. Methods: Eighteen 6-8 weeks mice were divided into three groups: control, 15Gy and 30Gy irradiated groups. The animal protocol was approved by the animal care ethics committee of our institution. Precision radiotherapy started with CT simulation, followed by treatment planning using volumetric modulated arc therapy (VMAT), image guidance with cone beam CT (CBCT) and radiation delivery on a medical linear accelerator. Weekly CT was conducted on the same CT simulator using same scanning parameters. At the end of fifth week, all mice were sacrificed, and histological staining was performed. Body weight, liver volume, HU values and histogram distributions were analyzed. Results: Body weight of irradiation groups was significantly reduced compared to that of the control group ( Conclusion: Longitudinal unenhanced CT is a useful imaging tool to evaluate the severity and progression of radiation induced liver injury.

Indexed as

longitudinal computed tomographyradiation induced liver diseaseradiation induced liver injury

Identifiers

PMID40337459
PMCPMC11934901

What OpenQuestion holds

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Registered trials

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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.