Evidence map›Paper›PMID 22745220›Full record

ArticleRadiographics : a review publication of the Radiological Society of North America, Inc

Informatics in radiology: improving clinical work flow through an AIM database: a sample web-based lesion tracking application.

Aaron C Abajian, Mia Levy, Daniel L Rubin

Abstract read
In one paragraph

Article in Radiographics : a review publication of the Radiological Society of North America, Inc. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.5field-weighted citation impact, top 11% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Ontology-based image navigation: exploring 3.0-T MR neurography of the brachial plexus using AIM and RadLex.Radiographics : a review publication of the Radiological Society of North America, Inc
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Aaron C AbajianDepartments of Radiology and Medicine, Stanford University School of Medicine, Richard M. Lucas Center, Stanford University, Stanford, CA 94305, USA.
Mia Levy
Daniel L Rubin
Stanford University · US

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Computing, Optimizing, and Evaluating Quantitative Cancer Imaging BiomarkersU01CA187947 · NCI · STANFORD UNIVERSITY · PI NAPEL, SANDY A., RUBIN, DANIEL L · 2015 to 2019
$2.9M
Computerized Quantitative Imaging Assessment of Tumor BurdenU01CA142555 · NCI · STANFORD UNIVERSITY · PI RUBIN, DANIEL L · 2010 to 2014
$2.9M
NCI NIH HHS U01 CA142555NCI NIH HHS U01CA142555-01NCI NIH HHS U01 CA187947NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

Quantitative assessments on images are crucial to clinical decision making, especially in cancer patients, in whom measurements of lesions are tracked over time. However, the potential value of quantitative approaches to imaging is impeded by the difficulty and time-intensive nature of compiling this information from prior studies and reporting corresponding information on current studies. The authors believe that the quantitative imaging work flow can be automated by making temporal data computationally accessible. In this article, they demonstrate the utility of the Annotation and Image Markup standard in a World Wide Web-based application that was developed to automatically summarize prior and current quantitative imaging measurements. The system calculates the Response Evaluation Criteria in Solid Tumors metric, along with several alternative indicators of cancer treatment response, by using the data stored in the annotation files. The application also allows the user to overlay the recorded metrics on the original images for visual inspection. Clinical evaluation of the system demonstrates its potential utility in accelerating the standard radiology work flow and in providing a means to evaluate alternative response metrics that are difficult to compute by hand. The system, which illustrates the utility of capturing quantitative information in a standard format and linking it to the image from which it was derived, could enhance quantitative imaging in clinical practice without adversely affecting the current work flow.

Indexed as

InternetUser-Computer InterfaceWorkflowData MiningHumansNeoplasmsRadiologyRadiology Information Systems

Identifiers

PMID22745220
PMCPMC3439633
OpenAlexW2117503872

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.