Evidence map›Paper›PMID 38136430›Full record

ReviewCancers2023

ImmunoPET Targeting Receptor Tyrosine Kinase: Clinical Applications.

Flavia Linguanti, Elisabetta Maria Abenavoli, Raffaella Calabretta, Valentina Berti, Egesta Lopci

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.9field-weighted citation impact, top 19% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 5 institutions in 2 countries.

Flavia LinguantiNuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50134 Florence, Italy.ORCID 0000-0001-9954-5032
Elisabetta Maria AbenavoliNuclear Medicine Division, Careggi University Hospital, 50134 Florence, Italy.
Raffaella CalabrettaDivision of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria.ORCID 0000-0002-8018-3884
Valentina BertiNuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50134 Florence, Italy.ORCID 0000-0001-9947-6559
Egesta LopciNuclear Medicine Unit, IRCCS-Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Italy.ORCID 0000-0001-9732-1094
Azienda Ospedaliero-Universitaria Careggi · ITIRCCS Humanitas Research Hospital · ITMedical University of Vienna · ATOspedale San Donato · ITUniversity of Florence · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Receptor tyrosine kinases, or RTKs, are one large family of cell surface receptors involved in signal transduction, which represent an integral part of the signaling pathways. They play a crucial role in most important cellular processes, starting with the cell cycle, proliferation and differentiation, as well as cell migration, metabolism and survival. The introduction of ImmunoPET evaluating the expression of RTKs by specific monoclonal antibodies (mAbs) or antibody fragments is regarded as a promising tool for imaging treatment efficacy and developing anticancer therapeutics. Our review focuses mainly on the current clinical research regarding ImmunoPET targeting RTKs, with particular interest in the epidermal growth factor family, or HER family, and vascular endothelial-derived growth factor/receptor.

Indexed as

EGFRHER2HER3ImmunoPETPET/CTreceptor tyrosine kinasesRTKVEGF

Identifiers

PMID38136430
PMCPMC10741542
OpenAlexW4389916965

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.