Evidence map›Paper›PMID 40226507›Full record

ReviewNpj imaging2025

A critical assessment of artificial intelligence in magnetic resonance imaging of cancer.

Chengyue Wu, Meryem Abbad Andaloussi, David A Hormuth, Ernesto A B F Lima, Guillermo Lorenzo, Casey E Stowers, Sriram Ravula, Brett Levac, Alexandros G Dimakis, Jonathan I Tamir and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Npj imaging, 2025. 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. The future of mathematical oncology in the age of AI.NPJ systems biology and applications · 2026
    Review
  3. Hybrid FeInternational journal of molecular sciences · 2026
    Article
  4. Article
  5. 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

13 authors.

Chengyue WuDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX USA.
Meryem Abbad AndaloussiFaculty of Science, Technology and Medicine University of Luxembourg, Belvaux, Luxembourg.
David A HormuthOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX USA.
Ernesto A B F LimaOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX USA.
Guillermo LorenzoOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX USA.
Casey E StowersOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX USA.
Sriram RavulaChandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX USA.
Brett LevacChandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX USA.
Alexandros G DimakisChandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX USA.
Jonathan I TamirOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX USA.
Kristy K BrockDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX USA.
Caroline ChungInstitute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX USA.
Thomas E YankeelovDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
Imaging-based tumor forecasting to predict brain tumor progression and response to therapyR01CA260003 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI QUARLES, CHRISTOPHER CHAD, YANKEELOV, THOMAS E · 2022 to 2025
$3.2M
INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCERU24CA226110 · NCI · BAYLOR COLLEGE OF MEDICINE · PI LEWIS, MICHAEL T., RUBIN, DANIEL L · 2019 to 2023
$3.2M
Using label-free Raman microscopy to predict therapeutic resistance of TNBC cellsU01CA253540 · NCI · UNIVERSITY OF TEXAS AT AUSTIN · PI BROCK, AMY, YANKEELOV, THOMAS E · 2020 to 2024
$2.1M
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA260003NCI NIH HHS U01 CA253540NCI NIH HHS U24 CA226110
6 · The paper itself

Abstract

Given the enormous output and pace of development of artificial intelligence (AI) methods in medical imaging, it can be challenging to identify the true success stories to determine the state-of-the-art of the field. This report seeks to provide the magnetic resonance imaging (MRI) community with an initial guide into the major areas in which the methods of AI are contributing to MRI in oncology. After a general introduction to artificial intelligence, we proceed to discuss the successes and current limitations of AI in MRI when used for image acquisition, reconstruction, registration, and segmentation, as well as its utility for assisting in diagnostic and prognostic settings. Within each section, we attempt to present a balanced summary by first presenting common techniques, state of readiness, current clinical needs, and barriers to practical deployment in the clinical setting. We conclude by presenting areas in which new advances must be realized to address questions regarding generalizability, quality assurance and control, and uncertainty quantification when applying MRI to cancer to maintain patient safety and practical utility.

Indexed as

Biomedical engineeringCancer imagingImage processing

Identifiers

PMID40226507
PMCPMC11981920

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.