Evidence map›Paper›PMID 42724322›Full record

ReviewAmerican journal of cancer research2026

Advances in nanotechnology for early cancer diagnosis: emerging nanoplatforms and multimodal imaging approaches.

Yuxin Mao, Muhammad Jamil, Dianhui Yang

Abstract readReview
In one paragraph

Review in American journal of cancer research, 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

3 authors.

Yuxin MaoCollege of Acupuncture and Tuina, Shandong University of Traditional Chinese Medicine Jinan 250014, Shandong, China.
Muhammad JamilArid Zone Research Center Dera Ismail Khan 29050, Pakistan.
Dianhui YangCollege of Acupuncture and Tuina, Shandong University of Traditional Chinese Medicine Jinan 250014, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanotechnology has revolutionized the field of cancer diagnosis, by enabling the development of innovative platforms for diagnosis that offer the potential to identify malignancies at an early stage, and they possess an unprecedented degree of sensitivity and specificity. This review focuses on the combination of nanotechnology and different imaging modality such as magnetic resonance imaging (MRI), positron emission tomography (PET), single photon emission computed tomography (SPECT) and optical imaging which demonstrate the potential to target tumour with high precision. We delve into the physicochemical characteristics of nanoparticles such as size, surface charge and functionalization that allow nanoparticles to cross the biological barriers, preferentially accumulate in tumor sites and deliver diagnostic and therapeutic payloads. Special attention is paid to the achievements in multimodal and theranostics platforms, where a single nanoparticle is able to perform both imaging and therapy at the same time. While these have great potential, there are challenges with clinical translation of these technologies ranging from biodistribution, toxicity, immunogenicity and regulatory hurdles. Despite these hurdles the accumulating amount of clinical evidence indicates that nanotechnology will have a significant role in precision oncology that will move from the experimental stages to routine clinical use, thus changing the paradigms of cancer detection and treatment.

Indexed as

molecular imagingmultimodal nanoplatformsNanotechnology-driven imagingprecision diagnosticstheranostic nanocarriers

Identifiers

PMID42724322
PMCPMC13559322

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.