Evidence map›Paper›PMID 40149980›Full record

ReviewBiomolecules2025

Nanomaterial-Based Molecular Imaging in Cancer: Advances in Simulation and AI Integration.

James C L Chow

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biomolecules, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Article
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Review
  17. Review
  18. Review
  19. Review
  20. Smart nanoplatforms for early detection and immune modulation in lung cancer.Frontiers in bioengineering and biotechnology · 2025
    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

1 author.

James C L ChowRadiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1X6, Canada.ORCID 0000-0003-4202-4855

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanomaterials represent an innovation in cancer imaging by offering enhanced contrast, improved targeting capabilities, and multifunctional imaging modalities. Recent advancements in material engineering have enabled the development of nanoparticles tailored for various imaging techniques, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US). These nanoscale agents improve sensitivity and specificity, enabling early cancer detection and precise tumor characterization. Monte Carlo (MC) simulations play a pivotal role in optimizing nanomaterial-based imaging by modeling their interactions with biological tissues, predicting contrast enhancement, and refining dosimetry for radiation-based imaging techniques. These computational methods provide valuable insights into nanoparticle behavior, aiding in the design of more effective imaging agents. Moreover, artificial intelligence (AI) and machine learning (ML) approaches are transforming cancer imaging by enhancing image reconstruction, automating segmentation, and improving diagnostic accuracy. AI-driven models can also optimize MC-based simulations by accelerating data analysis and refining nanoparticle design through predictive modeling. This review explores the latest advancements in nanomaterial-based cancer imaging, highlighting the synergy between nanotechnology, MC simulations, and AI-driven innovations. By integrating these interdisciplinary approaches, future cancer imaging technologies can achieve unprecedented precision, paving the way for more effective diagnostics and personalized treatment strategies.

Indexed as

Artificial IntelligenceMolecular ImagingNanostructuresNeoplasmsAnimalsContrast MediaHumansMachine LearningMagnetic Resonance ImagingMonte Carlo MethodContrast Mediaartificial intelligencecancer therapyCTmachine learningmedical imagingMonte Carlo simulationMRInanomaterialsnanoparticlesPAIPETUS

Identifiers

PMID40149980
PMCPMC11940464

What Socratic holds

Textmetadata
LicenceCC BY
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.