Evidence map›Paper›PMID 34374098›Full record

ArticleMedical physics2022

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy.

Chunhao Wang, Kyle R Padgett, Min-Ying Su, Eric A Mellon, Danilo Maziero, Zheng Chang

Open access · greenAbstract read
In one paragraph

Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

13 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Review
  3. Multi-center diffusion-weighted MRI validation for 0.35T MR-Linac.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Implementation and evaluation of a dynamic contrast-enhanced MR perfusion protocol for glioblastoma using a 0.35 T MRI-Linac system.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2024
    Article
  11. Review
  12. Article
  13. 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 3 institutions in 2 countries.

Chunhao WangDepartment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Kyle R PadgettDepartment of Radiation Oncology, University of Miami, Miami, Florida, USA.
Min-Ying SuDepartment of Radiological Sciences, University of California, Irvine, California, USA.
Eric A MellonDepartment of Radiation Oncology, University of Miami, Miami, Florida, USA.
Danilo MazieroDepartment of Radiation Oncology, University of Miami, Miami, Florida, USA.
Zheng ChangDepartment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
University of Miami · USDuke University · USUniversity of California, Irvine · US

Funding

Univ.of Calif., Irvine Cancer Center Support GrantP30CA062203 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Melanie Funes · 1994 to 2026
$57.9M
A Physiologic Adaptive Radiation Therapy Pipeline for Glioblastoma by Daily Multiparametric MRI and Machine LearningR37CA262510 · NCI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI Eric Albert Mellon · 2022 to 2026
$2.8M
NCI NIH HHS P30 CA062203NCI NIH HHS R37 CA262510
6 · The paper itself

Abstract

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

Indexed as

Multiparametric Magnetic Resonance ImagingHumansMagnetic Resonance Imagingmultiparametric MRIresponse assessmentRT

Identifiers

PMID34374098
PMCPMC9769892
OpenAlexW3190507727

What OpenQuestion holds

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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.