Evidence map›Paper›PMID 34649914›Full record

ReviewAJNR. American journal of neuroradiology2022

Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology.

M Ak, S A Toll, K Z Hein, R R Colen, S Khatua

Open access · bronzeAbstract readReview
In one paragraph

Review in AJNR. American journal of neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

  1. Pooled it
  2. Computational Pipeline in Neuroradiomics.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  3. Prediction of neoadjuvant chemotherapy efficacy in patients with HER2-low breast cancer based on ultrasound radiomics.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
    Article
  4. Article
  5. Article
  6. IntegratingBMC cancer · 2024
    Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Insights into imaging · 2022
    Article
  18. Review
  19. Review
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

5 authors at 4 institutions in 1 country.

M AkFrom the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.ORCID 0000-0001-7384-478X
S A TollDepartment of Hematology-Oncology (S.A.T.), Children's Hospital of Michigan, Detroit, Michigan.ORCID 0000-0003-0015-7609
K Z HeinDepartment of Leukemia (K.Z.H.), The University of Texas MD Anderson Cancer Center, Houston, Texas.ORCID 0000-0002-1247-9632
R R ColenFrom the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.ORCID 0000-0002-0882-0607
S KhatuaDepartment of Pediatric Hematology-Oncology (S.K.), Mayo Clinic, Rochester, Minnesota. khatua.soumen@mayo.edu.ORCID 0000-0001-6416-650X
University of Pittsburgh · USChildren's Hospital of Michigan · USMayo Clinic · USThe University of Texas MD Anderson Cancer Center · US

Funding

VECTOR CORE FACILITYP30CA047904 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI JOHN C. BYRD · 1988 to 2026
$158.0M
NCI NIH HHS P30 CA047904
6 · The paper itself

Abstract

Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.

Indexed as

NeoplasmsNeurologyArtificial IntelligenceChildDiagnostic ImagingGenomicsHumans

Identifiers

PMID34649914
PMCPMC9172943
OpenAlexW3207817659

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.