ArticleJournal of thoracic disease2010
Medical imaging in new drug clinical development.
Article in Journal of thoracic disease, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pharmaco-psychiatry and gut microbiome: a systematic review of effects of psychotropic drugs for bipolar disorder.Microbiology (Reading, England) · 2025Pooled it
- Real-World Imaging Data: Opportunities and Challenges.JMIR medical informatics · 2026Article
- Imaging biomarkers for evaluating tumor response: RECIST and beyond.Biomarker research · 2021Review
- Tumor Drug Penetration Measurements Could Be the Neglected Piece of the Personalized Cancer Treatment Puzzle.Clinical pharmacology and therapeutics · 2019Review
- Neutrophil Elastase Activity Imaging: Recent Approaches in the Design and Applications of Activity-Based Probes and Substrate-Based Probes.Contrast media & molecular imaging · 2019Review
- Liver intravoxel incoherent motion (IVIM) magnetic resonance imaging: a comprehensive review of published data on normal values and applications for fibrosis and tumor evaluation.Quantitative imaging in medicine and surgery · 2017Review
- T1ρ magnetic resonance: basic physics principles and applications in knee and intervertebral disc imaging.Quantitative imaging in medicine and surgery · 2015Review
- Recent advances in superparamagnetic iron oxide nanoparticles for cellular imaging and targeted therapy research.Current pharmaceutical design · 2013Review
- The impact of quantitative imaging in medicine and surgery: Charting our course for the future.Quantitative imaging in medicine and surgery · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical imaging can help answer key questions that arise during the drug development process. The role of medical imaging in new drug clinical trials includes identification of likely responders; detection and diagnosis of lesions and evaluation of their severity; and therapy monitoring and follow-up. Nuclear imaging techniques such as PET can be used to monitor drug pharmacokinetics and distribution and study specific molecular endpoints. In assessing drug efficacy, imaging biomarkers and imaging surrogate endpoints can be more objective and faster to measure than clinical outcomes, and allow small group sizes, quick results and good statistical power. Imaging also has important role in drug safety monitoring, particularly when there is no other suitable biomarkers available. Despite the long history of radiological sciences, its application to the drug development process is relatively recent. This review highlights the processes, opportunities, and challenges of medical imaging in new drug development.
Indexed as
Identifiers
What OpenQuestion holds
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.