ArticleMagnetic resonance imaging2012
Informatics methods to enable sharing of quantitative imaging research data.
Article in Magnetic resonance imaging, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of the technical performance of an imaging procedure: guidelines and statistical methodology.Statistical methods in medical research · 2015Pooled it
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review).Oncology reports · 2024Review
- Sharing Data Is Essential for the Future of AI in Medical Imaging.Radiology. Artificial intelligence · 2024Article
- Bibliometrics research on radiomics of lung cancer.Translational cancer research · 2021Article
- ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging.Tomography (Ann Arbor, Mich.) · 2019Article
- Special Section Guest Editorial: Quantitative Imaging and the Pioneering Efforts of Laurence P. Clarke.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Development and clinical application of radiomics in lung cancer.Radiation oncology (London, England) · 2017Review
- Automated tracking of quantitative assessments of tumor burden in clinical trials.Translational oncology · 2014Article
- Towards a semantic PACS: Using Semantic Web technology to represent imaging data.Studies in health technology and informatics · 2014Article
- Quantitative imaging in cancer evolution and ecology.Radiology · 2013Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
introductionThe National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community.
methodsWe performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN.
resultsThere are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network.
conclusionsAs a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel imaging biomarkers.
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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.