Evidence map›Paper›PMID 29432550›Full record

ArticleJournal of radiation research2018

Strategies for prediction and mitigation of radiation-induced liver toxicity.

Diego A S Toesca, Bulat Ibragimov, Amanda J Koong, Lei Xing, Albert C Koong, Daniel T Chang

Open access · goldAbstract read
In one paragraph

Article in Journal of radiation research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 11% 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

31 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.

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  18. Radiation-induced liver disease: beyond DNA damage.Cell cycle (Georgetown, Tex.) · 2023
    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

6 authors at 1 institution in 1 country.

Diego A S ToescaDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Bulat IbragimovDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Amanda J KoongDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Lei XingDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Albert C KoongDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Daniel T ChangDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Stanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although well described in the 1960s, liver toxicity secondary to radiation therapy, commonly known as radiation-induced liver disease (RILD), remains a major challenge. RILD encompasses two distinct clinical entities, a 'classic' form, composed of anicteric hepatomegaly, ascites and elevated alkaline phosphatase; and a 'non-classic' form, with liver transaminases elevated to more than five times the reference value, or worsening of liver metabolic function represented as an increase of 2 or more points in the Child-Pugh score classification. The risk of occurrence of RILD has historically limited the applicability of radiation for the treatment of liver malignancies. With the development of 3D conformal radiation therapy, which allowed for partial organ irradiation based on computed tomography treatment planning, there has been a resurgence of interest in the use of liver irradiation. Since then, a large body of evidence regarding the liver tolerance to conventionally fractionated radiation has been produced, but severe liver toxicities has continued to be reported. More recently, improvements in diagnostic imaging, radiation treatment planning technology and delivery systems have prompted the development of stereotactic body radiotherapy (SBRT), by which high doses of radiation can be delivered with high target accuracy and a steep dose gradient at the tumor - normal tissue interface, offering an opportunity of decreasing toxicity rates while improving tumor control. Here, we present an overview of the role SBRT has played in the management of liver tumors, addressing the challenges and opportunities to reduce the incidence of RILD, such as adaptive approaches and machine-learning-based predictive models.

Indexed as

Dose Fractionation, RadiationHepatitis B virusHumansLiverLiver DiseasesMachine LearningPrognosisRadiation InjuriesRadiosurgeryVirus Activation

Identifiers

PMID29432550
PMCPMC5868188
OpenAlexW2791716363

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.