Evidence map›Paper›PMID 36710376›Full record

ReviewMedical physics2023

Multi-parametric MRI for radiotherapy simulation.

Tian Li, Jihong Wang, Yingli Yang, Carri K Glide-Hurst, Ning Wen, Jing Cai

Abstract readReview
In one paragraph

Review in Medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  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. The use of PET/MRI in radiotherapy.Insights into imaging · 2024
    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

6 authors.

Tian LiDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Jihong WangDepartment of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA.
Yingli YangDepartment of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China.
Carri K Glide-HurstDepartment of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA.
Ning WenDepartment of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China.
Jing CaiDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.

Funding

Reducing cardiac toxicity with deep learning and MRI-guided radiation therapyR01HL153720 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI GLIDE-HURST, CARRI KAYE · 2021 to 2024
$2.2M
Toward precision radiotherapy: Physiological modeling of respiratory motion based on ultra-quality 4D-MRIR01CA226899 · NCI · UNIVERSITY OF VIRGINIA · PI CAI, JING, MILLER, G. WILSON · 2019 to 2023
$2.2M
Development of Anatomical Patient Models to Facilitate MR-only Treatment PlanningR01CA204189 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI GLIDE-HURST, CARRI KAYE · 2016 to 2020
$1.5M
NCI NIH HHS R01 CA204189NCI NIH HHS R01 CA226899NHLBI NIH HHS R01 HL153720
6 · The paper itself

Abstract

Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.

Indexed as

Magnetic Resonance ImagingRadiotherapyartificial intelligencemachine learningmpMRIMRI-guided radiotherapyMRI-linacMRI simulationmulti-parametric MRI

Identifiers

PMID36710376
PMCPMC10382603

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.