ReviewCancer imaging : the official publication of the International Cancer Imaging Society2024
Exploring innovative strides in radiolabeled nanoparticle progress for multimodality cancer imaging and theranostic applications.
Review in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed.
- 4D nanoimaging-guided smart therapeutics: spatiotemporal control of disease microenvironments.RSC advances · 2026Review
- Bridging In Vitro and Murine Breast Cancer Models: Advanced Imaging Across Multiscale Experimental Platforms.Cancers · 2026Review
- Multifunctional Nano-Contrast Agent Carriers: From Traditional Platforms to Next-Generation Theranostic Applications in Molecular Imaging.Biomedicines · 2026Review
- Next-generation biomedical nanorobots: active design, intelligent control, and translational opportunities in regenerative and minimally invasive medicine.Journal of nanobiotechnology · 2026Review
- Application of multimodal nanotechnology and standardized nursing management in ventricular arrhythmia.Biomedical engineering online · 2026Review
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Enhancing the Safety of Nanoparticles in Medicine: Highlights of Progress and Critical Objectives.International journal of nanomedicine · 2026Review
- Biomimetic nanoparticles in cancer photodynamic therapy: a review of targeted delivery systems and therapeutic outcomes.Beilstein journal of nanotechnology · 2026Review
- Advances in nanotechnology for early cancer diagnosis: emerging nanoplatforms and multimodal imaging approaches.American journal of cancer research · 2026Review
- Theranostics in the management of colorectal cancer.Discover nano · 2025Review
- Polymeric Nanoparticles for Targeted Lung Cancer Treatment: Review and Perspectives.Pharmaceutics · 2025Review
- Recent Advances in Micro- and Nano-Enhanced Intravascular Biosensors for Real-Time Monitoring, Early Disease Diagnosis, and Drug Therapy Monitoring.Sensors (Basel, Switzerland) · 2025Review
- Nanoradiopharmaceuticals: Design Principles, Radiolabeling Strategies, and Biomedicine Applications.Pharmaceutics · 2025Review
- In situ radiochemical doping of functionalized inorganic nanoplatforms for theranostic applications: a paradigm shift in nanooncology.Journal of nanobiotechnology · 2025Review
- Nano-Radiopharmaceuticals in Colon Cancer: Current Applications, Challenges, and Future Directions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- A review of state-of-the-art resolution improvement techniques in SPECT imaging.EJNMMI physics · 2025Review
- An overview of the role of radionuclides in targeted cancer treatment: application of biomarkers for patient selection and developments to improve treatment efficacy.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multimodal imaging unfolds as an innovative approach that synergistically employs a spectrum of imaging techniques either simultaneously or sequentially. The integration of computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and optical imaging (OI) results in a comprehensive and complementary understanding of complex biological processes. This innovative approach combines the strengths of each method and overcoming their individual limitations. By harmoniously blending data from these modalities, it significantly improves the accuracy of cancer diagnosis and aids in treatment decision-making processes. Nanoparticles possess a high potential for facile functionalization with radioactive isotopes and a wide array of contrast agents. This strategic modification serves to augment signal amplification, significantly enhance image sensitivity, and elevate contrast indices. Such tailored nanoparticles constructs exhibit a promising avenue for advancing imaging modalities in both preclinical and clinical setting. Furthermore, nanoparticles function as a unified nanoplatform for the co-localization of imaging agents and therapeutic payloads, thereby optimizing the efficiency of cancer management strategies. Consequently, radiolabeled nanoparticles exhibit substantial potential in driving forward the realms of multimodal imaging and theranostic applications. This review discusses the potential applications of molecular imaging in cancer diagnosis, the utilization of nanotechnology-based radiolabeled materials in multimodal imaging and theranostic applications, as well as recent advancements in this field. It also highlights challenges including cytotoxicity and regulatory compliance, essential considerations for effective clinical translation of nanoradiopharmaceuticals in multimodal imaging and theranostic applications.
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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 OpenQuestion graph.