Evidence map›Paper›PMID 41784890›Full record

ReviewLa Radiologia medica2026

Radiologic exposomics: imaging the environmental imprint on cancer for precision oncology.

Andrea Delli Pizzi, Massimo Caulo

Abstract readReview
In one paragraph

Review in La Radiologia medica, 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.

Andrea Delli PizziITAB - Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University, Chieti, Italy. andreadellipizzi@gmail.com.ORCID http://orcid.org/0000-0002-2011-3753
Massimo CauloITAB - Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University, Chieti, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Environmental exposures-such as airborne pollutants, metals, and urban stressors-contribute to cancer development and progression, yet their downstream biological effects remain difficult to characterize in vivo. Quantitative medical imaging may help fill this gap. Radiomics, in particular, offers access to tissue-level patterns shaped by chronic injury and microenvironmental remodeling. In this review, we discuss the rationale for linking geospatial exposure assessment with CT- and MRI-derived imaging biomarkers and outline how radiologic features may reflect processes associated with long-term environmental stress, including oxidative damage, inflammation, and metabolic or immune dysregulation. We also summarize epidemiologic evidence across major cancer types to contextualize where imaging-exposure integration is most plausible. A methodological workflow is presented, covering exposure assignment, imaging standardization, feature extraction, and strategies for harmonizing and modeling high-dimensional exposomic and radiomic data. Considerations related to confounding, data governance, and equity are also addressed, as these factors are integral to responsible implementation. Viewed in this light, imaging can be interpreted as an intermediate phenotype of the exposome-capturing aspects of tumor and peritumoral biology influenced by external stressors. This perspective may expand the role of radiology in precision oncology and generate new hypotheses about how environmental conditions shape cancer biology.

Indexed as

Environmental ExposureExposomeMagnetic Resonance ImagingMedical OncologyNeoplasmsPrecision MedicineHumansRadiomicsTomography, X-Ray ComputedAir pollutionCancer imagingEnvironmental exposurePrecision oncologyRadiologic exposomicsRadiomics

Identifiers

PMID41784890
PMCPMC13279722

What Socratic holds

Textmetadata
LicenceCC BY
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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.