Evidence map›Paper›PMID 42373751›Full record

ReviewNature reviews. Cancer2026

Imaging the hallmarks of cancer.

Jan Grimm, Fabian Kiessling, Kevin M Brindle, Douglas Hanahan, Sanjay K Jain, Philippe Lambin, Twan Lammers, King C Li, I Jolanda M de Vries, Wolfgang A Weber and 2 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. 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

12 authors.

Jan GrimmMemorial Sloan Kettering Cancer Center, New York, NY, USA. grimmj@mskcc.org.
Fabian KiesslingInstitute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany. fkiessling@ukaachen.de.
Kevin M BrindleUniversity of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0003-3883-6287
Douglas HanahanSwiss Institute for Experimental Cancer Research (ISREC), EPFL, Lausanne, Switzerland.ORCID http://orcid.org/0000-0002-0883-5251
Sanjay K JainCincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.ORCID http://orcid.org/0000-0001-9620-7070
Philippe LambinDepartment of Precision Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.ORCID http://orcid.org/0000-0001-7961-0191
Twan LammersInstitute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.ORCID http://orcid.org/0000-0002-1090-6805
King C LiCarle Illinois College of Medicine, Urbana, IL, USA.ORCID http://orcid.org/0000-0002-6473-2687
I Jolanda M de VriesMedical BioSciences, Radboud University Medical Centre, Nijmegen, The Netherlands.
Wolfgang A WeberTUM University Hospital and Bavarian Cancer Research Center (BZKF), Munich, Germany.ORCID http://orcid.org/0000-0002-7854-4345
Bettina WeigelinWerner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany.ORCID http://orcid.org/0000-0003-4460-3113
Bernd J PichlerWerner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany. bernd.pichler@med.uni-tuebingen.de.ORCID http://orcid.org/0000-0001-6784-5524

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The hallmarks of cancer were introduced by Hanahan and Weinberg as a conceptual organizing framework to distil the complexity of tumours. This concept of cancer hallmarks has become an enduring theme in cancer research. Moreover, an increasing number of therapeutic strategies are being aimed at targeting these hallmarks. However, translating them into the clinic requires technologies to monitor their effectiveness and biomarkers that can stratify patients for the choice of specific therapies. Tumour heterogeneity and the ability of tumour cells to rapidly mutate and develop evasion strategies makes the development of non-invasive imaging capabilities to interrogate these hallmarks as biomarkers and monitor them longitudinally and quantitatively particularly important. This Review presents a holistic discussion of non-invasive diagnostic imaging capabilities related to the hallmarks of cancer; some hallmarks can be assessed with imaging probes that directly target biomolecules, whereas others can be interrogated indirectly by imaging pathophysiological processes. Additionally, visualizing the hallmarks of cancer can be addressed with artificial intelligence-assisted, multiparametric image analysis (for example, radiomics, radiogenomics and deep learning). The approaches discussed have been evaluated in a translational context, and some of them already have a substantial role in clinical practice, for example, to guide treatment strategies, including surgical resections, radiotherapy and molecularly targeted chemo-, immuno- and radiopharmaceutical therapies.

Indexed as

Diagnostic ImagingNeoplasmsAnimalsBiomarkers, TumorHumansRadiomicsBiomarkers, Tumor

Identifiers

What Socratic 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 Socratic graph.