Evidence map›Paper›PMID 42149190›Full record

ReviewRecent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer2026

Preclinical PET, PET/CT, and PET/MRI.

Sonja Schelhaas, Michael Schäfers

Abstract readReview
PubMed Publisher
In one paragraph

Review in Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer, 2026. 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
–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

0 citing papers in PubMed.

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

2 authors.

Sonja SchelhaasEuropean Institute for Molecular Imaging, Multiscale Imaging Centre (MIC), University of Münster, Münster, Germany. sonja.schelhaas@uni-muenster.de.
Michael SchäfersEuropean Institute for Molecular Imaging, Multiscale Imaging Centre (MIC), University of Münster, Münster, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As tumors represent one of the most common causes of death in developed countries, it is of utmost importance to understand the (molecular) mechanisms underlying tumor progression. On this basis, cancer diagnosis and therapy can be improved. Specific radioactive molecules can help in providing such information when detected by sensitive cameras, especially when this is done in a three-dimensional manner. Positron-emitting radioisotopes can be detected by positron emission tomography (PET) cameras. A range of molecular processes can be visualized by PET, depending on the radiotracer used. However, before novel tracers can be used in a clinical scenario, it is fundamental to thoroughly prove their specificity in in vitro experiments and also in animal models. And also tracers, which are already clinically used, may be further validated in preclinical studies. To this end, small animal models of cancer and also dedicated preclinical PET cameras have been developed. Preclinical PET, in combination with CT or MRI, has fundamentally contributed to the establishment of novel imaging, diagnostic, therapy and theranostic approaches in the clinics. Here, we provide an overview of the diverse applications and also the challenges of preclinical PET in the field of oncology.

Indexed as

Magnetic Resonance ImagingMultimodal ImagingNeoplasmsPositron-Emission TomographyPositron Emission Tomography Computed TomographyAnimalsHumansRadiopharmaceuticalsRadiopharmaceuticalsOncology modelsPositron emission tomographyPreclinical imagingSmall animal imagingTumor characterization

Identifiers

PMID42149190

What OpenQuestion holds

Textmetadata
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.