Evidence map›Paper›PMID 40837043›Full record

ReviewSmall science2025

Radiolabeled Nanogels: From Multimodality Imaging to Combination Therapy of Cancer.

Sanchita Ghosh, Weibo Cai, Rubel Chakravarty

Abstract readReview
In one paragraph

Review in Small science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
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.

Sanchita GhoshRadiopharmaceuticals Division Bhabha Atomic Research Centre Trombay Mumbai 400085 India.
Weibo CaiDepartments of Radiology and Medical Physics University of Wisconsin-Madison Madison WI 53705 USA.ORCID https://orcid.org/0000-0003-4641-0833
Rubel ChakravartyRadiopharmaceuticals Division Bhabha Atomic Research Centre Trombay Mumbai 400085 India.ORCID https://orcid.org/0000-0003-2125-8636

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advancements in nanotechnology over the past few decades have offered tremendous possibilities toward cancer theranostics. Radiolabeled nanogels (NGs) represent a promising nanoplatform in this direction, offering a multifunctional toolset for both imaging and therapeutic interventions. This review encapsulates the progressions and potential of radiolabeled NGs in the realm of cancer research. Firstly, multifunctional radiolabeled NGs serve as potent contrast agents for multimodality imaging, enabling precise visualization of tumor sites through various techniques such as positron emission tomography, single-photon emission computed tomography, optical imaging and magnetic resonance imaging. Furthermore, by incorporating more than one therapeutic payload such as chemotherapeutic drugs, nucleic acids, and/or therapeutic radionuclides, they enable synergistic treatment modalities that address the heterogeneity of cancer cells and their microenvironment. This combination approach allows for enhanced therapeutic efficacy while minimizing systemic toxicity, addressing challenges associated with conventional cancer therapies. Furthermore, the radiolabeling of NGs provides a means for real-time monitoring of therapeutic distribution and pharmacokinetics, offering valuable insights into treatment response and optimization. Overall, radiolabeled NGs represent a promising platform for the integration of multimodality imaging and combination therapy in the fight against cancer with increased efficacy, reduced toxicity, and improved patient outcomes.

Indexed as

cancercombination therapydrug deliverymultimodality imagingnanogel

Identifiers

PMID40837043
PMCPMC12362799

What OpenQuestion holds

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Registered trials

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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.