Evidence map›Paper›PMID 33200013›Full record

ReviewWorld journal of hepatology2020

Hepatocellular carcinoma Liver Imaging Reporting and Data Systems treatment response assessment: Lessons learned and future directions.

Anum Aslam, Richard Kinh Gian Do, Avinash Kambadakone, Bradley Spieler, Frank H Miller, Ahmed M Gabr, Resmi A Charalel, Charles Y Kim, David C Madoff, Mishal Mendiratta-Lala

Open access · diamondAbstract readReview
In one paragraph

Review in World journal of hepatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.9field-weighted citation impact, top 13% 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

18 citing papers in PubMed, 22 citations in OpenAlex.

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  18. Abbreviated MRI Protocol for the Assessment of Ablated Area in HCC Patients.International journal of environmental research and public health · 2021
    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

10 authors at 3 institutions in 1 country.

Anum AslamDepartment of Radiology, University of Michigan, Ann Arbor, MI 48019, United States. aanum@med.umich.edu.
Richard Kinh Gian DoDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.
Avinash KambadakoneAbdominal Imaging and Interventional Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, United States.
Bradley SpielerDepartment of Radiology, Louisiana State University Health Sciences Center, New Orleans, LA 70112, United States.
Frank H MillerDepartment of Radiology, Northwestern University Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States.
Ahmed M GabrDepartment of Interventional Radiology, OHSU and Tanta University, Egypt, Portland, OR 97239, United States.
Resmi A CharalelDepartment of Radiology, Weill Cornell Medicine, New York, NY 10065, United States.
Charles Y KimDepartment of Radiology, Duke University Medical Center, Duke University, Durham, NC 27710, United States.
David C MadoffDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States.
Mishal Mendiratta-LalaSchool of Medicine, 1500 East Medical Center Drive, University of Michigan, Ann Arbor, MI 48109, United States.
Cornell University · USMemorial Sloan Kettering Cancer Center · USYale University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
TREATMENT VERIFICATION AND TREATMENT PLAN REFINEMENTP01CA059827 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KESSLER, MARC LEWIS, LAWRENCE, THEODORE S · 1993 to 2018
$37.9M
NCATS NIH HHS UL1 TR001863NCI NIH HHS P01 CA059827NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is a leading cause of morbidity and mortality worldwide, with rising clinical and economic burden as incidence increases. There are a multitude of evolving treatment options, including locoregional therapies which can be used alone, in combination with each other, or in combination with systemic therapy. These treatment options have shown to be effective in achieving remission, controlling tumor progression, improving disease free and overall survival in patients who cannot undergo resection and providing a bridge to transplant by debulking tumor burden to downstage patients. Following locoregional therapy (LRT), it is crucial to provide treatment response assessment to guide management and liver transplant candidacy. Therefore, Liver Imaging Reporting and Data Systems (LI-RADS) Treatment Response Algorithm (TRA) was created to provide a standardized assessment of HCC following LRT. LI-RADS TRA provides a step by step approach to evaluate each lesion independently for accurate tumor assessment. In this review, we provide an overview of different locoregional therapies for HCC, describe the expected post treatment imaging appearance following treatment, and review the LI-RADS TRA with guidance for its application in clinical practice. Unique to other publications, we will also review emerging literature supporting the use of LI-RADS for assessment of HCC treatment response after LRT.

Indexed as

Arterial phase hyper enhancementHepatocellular carcinomaLiver Imaging Reporting and Data Systems Treatment Response AlgorithmLiver Imaging Reporting and Data Systems Treatment Response equivocalLocoregional therapyStereotactic body radiotherapy

Identifiers

PMID33200013
PMCPMC7643220
OpenAlexW3093955124

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.