Evidence map›Paper›PMID 21799326›Full record

ReviewCancer journal (Sudbury, Mass.)

Current and future trends in imaging informatics for oncology.

Mia A Levy, Daniel L Rubin

Abstract readReview
In one paragraph

Review in Cancer journal (Sudbury, Mass.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Informatics in radiology: improving clinical work flow through an AIM database: a sample web-based lesion tracking application.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

2 authors.

Mia A LevyDepartment of Biomedical Informatics and Medicine, Division of Hematology and Oncology, Vanderbilt University, Nashville, TN, USA. mia.levy@vanderbilt.edu
Daniel L Rubin

Funding

Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant TherapyU01CA142565 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI KARCZMAR, GREGORY S., NANDA, RITA · 2010 to 2019
$4.5M
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 U01CA142565-01
6 · The paper itself

Abstract

Clinical imaging plays an essential role in cancer care and research for diagnosis, prognosis, and treatment response assessment. Major advances in imaging informatics to support medical imaging have been made during the last several decades. More recent informatics advances focus on the special needs of oncologic imaging, yet gaps still remain. We review the current state, limitations, and future trends in imaging informatics for oncology care including clinical and clinical research systems. We review information systems to support cancer clinical workflows including oncologist ordering of radiology studies, radiologist review and reporting of image findings, and oncologist review and integration of imaging information for clinical decision making. We discuss informatics approaches to oncologic imaging including, but not limited to, controlled terminologies, image annotation, and image-processing algorithms. With the ongoing development of novel imaging modalities and imaging biomarkers, we expect these systems will continue to evolve and mature.

Indexed as

Diagnostic ImagingAlgorithmsDecision MakingHumansImage Processing, Computer-AssistedMedical InformaticsMedical OncologyNeoplasmsTreatment Outcome

Identifiers

PMID21799326
PMCPMC4050062

What OpenQuestion holds

Textmetadata
LicenceTDM
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.