ReviewJournal of digital imaging2013
A novel knowledge representation framework for the statistical validation of quantitative imaging biomarkers.
Review in Journal of digital imaging, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Methods and challenges in quantitative imaging biomarker development.Academic radiology · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative imaging biomarkers are of particular interest in drug development for their potential to accelerate the drug development pipeline. The lack of consensus methods and carefully characterized performance hampers the widespread availability of these quantitative measures. A framework to support collaborative work on quantitative imaging biomarkers would entail advanced statistical techniques, the development of controlled vocabularies, and a service-oriented architecture for processing large image archives. Until now, this framework has not been developed. With the availability of tools for automatic ontology-based annotation of datasets, coupled with image archives, and a means for batch selection and processing of image and clinical data, imaging will go through a similar increase in capability analogous to what advanced genetic profiling techniques have brought to molecular biology. We report on our current progress on developing an informatics infrastructure to store, query, and retrieve imaging biomarker data across a wide range of resources in a semantically meaningful way that facilitates the collaborative development and validation of potential imaging biomarkers by many stakeholders. Specifically, we describe the semantic components of our system, QI-Bench, that are used to specify and support experimental activities for statistical validation in quantitative imaging.
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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.