ReviewNature reviews. Cancer2026
Imaging the hallmarks of cancer.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
42373751What Socratic holds
Registered trials
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.