ReviewInternational journal of molecular sciences2017
Drug Discovery by Molecular Imaging and Monitoring Therapy Response in Lymphoma.
Review in International journal of molecular sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 25 citations in OpenAlex.
- Development of a Dual-Isotope Imaging Technique for Simultaneous Quantification and Evaluation of Biodistribution of Antibodies Using "XCam-CdTe".Pharmaceutical research · 2026Article
- Mesoporous silica nanoparticles in glioblastoma: smart nano-platforms for targeted therapy and precision diagnosis.3 Biotech · 2026Review
- Doxorubicin-loaded PEGylated liposome modified with ANGPT2-specific peptide for integrative glioma-targeted imaging and therapy.Materials today. Bio · 2025Article
- Review
- AnFrontiers in bioengineering and biotechnology · 2022Article
- Recent progress in the molecular imaging of therapeutic monoclonal antibodies.Journal of pharmaceutical analysis · 2020Review
- Application of In Vivo Imaging Techniques for Monitoring Natural Killer Cell Migration and Tumor Infiltration.Cancers · 2020Review
- Molecular imaging in tracking cancer stem cells: A review.Medical journal of the Islamic Republic of Iran · 2020Review
- Radiolabeled PET/MRI Nanoparticles for Tumor Imaging.Journal of clinical medicine · 2019Review
- Migration of mesenchymal stem cells to tumor xenograft models andInternational journal of medical sciences · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular imaging allows a noninvasive assessment of biochemical and biological processes in living subjects. Treatment strategies for malignant lymphoma depend on histology and tumor stage. For the last two decades, molecular imaging has been the mainstay diagnostic test for the staging of malignant lymphoma and the assessment of response to treatment. This technology enhances our understanding of disease and drug activity during preclinical and clinical drug development. Here, we review molecular imaging applications in drug development, with an emphasis on oncology. Monitoring and assessing the efficacy of anti-cancer therapies in preclinical or clinical models are essential and the multimodal molecular imaging approach may represent a new stage for pharmacologic development in cancer. Monitoring the progress of lymphoma therapy with imaging modalities will help patients. Identifying and addressing key challenges is essential for successful integration of molecular imaging into the drug development process. In this review, we highlight the general usefulness of molecular imaging in drug development and radionuclide-based reporter genes. Further, we discuss the different molecular imaging modalities for lymphoma therapy and their preclinical and clinical 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.