Evidence map›Paper›PMID 36456637›Full record

ArticleScientific reports2022

Prediction of left lobe hypertrophy after right lobe radioembolization of the liver using a clinical data model with external validation.

Jens M Theysohn, Aydin Demircioglu, Malte Kleditzsch, Johannes M Ludwig, Manuel Weber, Lale Umutlu, Yan Li, Malte Kircher, Constantin Lapa, Andreas Buck and 8 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

18 authors at 6 institutions in 3 countries.

Jens M Theysohn *Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45122, Essen, Germany. jens.theysohn@uk-essen.de.
Aydin Demircioglu *Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45122, Essen, Germany.
Malte KleditzschDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45122, Essen, Germany.
Johannes M LudwigDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45122, Essen, Germany.
Manuel WeberClinic for Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Lale UmutluDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45122, Essen, Germany.
Yan LiDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45122, Essen, Germany.
Malte KircherNuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany.
Constantin LapaNuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany.
Andreas BuckClinic for Nuclear Medicine, University Hospital Wuerzburg, Wuerzburg, Germany.
Michael KoehlerRadiology, University Hospital Muenster, Muenster, Germany.
Moritz WildgruberDepartment for Radiology, University Hospital, LMU, Munich, Germany.
Christian M LangeClinic for Gastroenterology and Hepatology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Xavier PalardDepartment of Nuclear Medicine, Centre Eugène Marquis, Rennes, France.
Etienne GarinDepartment of Nuclear Medicine, Centre Eugène Marquis, Rennes, France.
Ken HerrmannClinic for Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Michael ForstingDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45122, Essen, Germany.
Felix NensaDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45122, Essen, Germany.
University of Duisburg-Essen · DECentre Eugène Marquis · FRUniversity of Augsburg · DELMU Klinikum · DEUniversitätsklinikum Würzburg · DEUniversity Hospital Münster · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In cirrhotic patients with hepatocellular carcinoma (HCC), right-sided radioembolization (RE) with Yttrium-90-loaded microspheres is an established palliative therapy and can be considered a "curative intention" treatment when aiming for sequential tumor resection. To become surgical candidate, hypertrophy of the left liver lobe to > 40% (future liver remnant, FLR) is mandatory, which can develop after RE. The amount of radiation-induced shrinkage of the right lobe and compensatory hypertrophy of the left lobe is difficult for clinicians to predict. This study aimed to utilize machine learning to predict left lobe liver hypertrophy in patients with HCC and cirrhosis scheduled for right lobe RE, with external validation. The results revealed that machine learning can accurately predict relative and absolute volume changes of the left liver lobe after right lobe RE. This prediction algorithm could help to estimate the chances of conversion from palliative RE to curative major hepatectomy following significant FLR hypertrophy.

Indexed as

BrachytherapyCarcinoma, HepatocellularLiver NeoplasmsHumansHypertrophy

Identifiers

PMID36456637
PMCPMC9715713
OpenAlexW4310704871

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.