Evidence map›Paper›PMID 32408309›Full record

ReviewOncology2020

Oncology Informatics: Status Quo and Outlook.

Paul Martin Putora, Michael Baudis, Beth M Beadle, Issam El Naqa, Frank A Giordano, Nils H Nicolay

Open access · bronzeAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 23% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Machine learning in the prediction of cancer therapy.Computational and structural biotechnology journal · 2021
    Review
  4. 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

6 authors at 6 institutions in 3 countries.

Paul Martin PutoraDepartment of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland, PaulMartin.Putora@kssg.ch.
Michael BaudisDepartment of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.
Beth M BeadleDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Issam El NaqaDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Frank A GiordanoDepartment of Radiation Oncology, University of Bonn, Bonn, Germany.
Nils H NicolayDepartment of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany.
SIB Swiss Institute of Bioinformatics · CHStanford University · USUniversity of Bern · CHUniversity of Bonn · DEUniversity of Freiburg · DEUniversity of Michigan · US

Funding

Optimal Decision Making in Radiotherapy Using Panomics AnalyticsR01CA233487 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EL NAQA, ISSAM M. · 2019 to 2023
$2.4M
NCI NIH HHS R01 CA233487
6 · The paper itself

Abstract

Oncology has undergone rapid progress, with emerging developments in areas including cancer stem cells, molecularly targeted therapies, genomic analyses, and individually tailored immunotherapy. These advances have expanded the tools available in the fight against cancer. Some of these have seen broad media coverage resulting in justified public attention. However, these achievements have only been possible due to rapid developments in the expanding field of biomedical informatics and information technology (IT). Artificial intelligence, radiomics, electronic health records, and electronic patient-reported outcome measures (ePROMS) are only a few of the developments enabling further progress in oncology. The promising impact of IT in oncology will only become reality through a multidisciplinary approach to the complex challenges ahead.

Indexed as

Artificial IntelligenceCommunicationHumansImmunotherapyMedical OncologyNeoplasmsPatient Reported Outcome MeasuresArtificial intelligenceCancerElectronic health recordElectronic patient-reported outcome measuresInformaticsMachine learningOncologyRadiation oncology

Identifiers

PMID32408309
PMCPMC8882056
OpenAlexW3024317127

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.