Evidence map›Paper›PMID 36012969›Full record

ReviewJournal of clinical medicine2022

History of Technological Advancements towards MR-Linac: The Future of Image-Guided Radiotherapy.

Nikhil Rammohan, James W Randall, Poonam Yadav

Abstract readReview
In one paragraph

Review in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–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

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024
    Pooled it
  2. Article
  3. Article
  4. Review
  5. Evaluating participant experiences and tolerability with MR Linac imaging.Technical innovations & patient support in radiation oncology · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
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  20. 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

3 authors.

Nikhil RammohanDepartment of Radiation Oncology, Northwestern Medicine, Chicago, IL 60611, USA.
James W RandallDepartment of Radiation Oncology, Northwestern Medicine, Chicago, IL 60611, USA.ORCID 0000-0003-4703-0779
Poonam YadavDepartment of Radiation Oncology, Northwestern Medicine, Chicago, IL 60611, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Image-guided radiotherapy (IGRT) enables optimal tumor targeting and sparing of organs-at-risk, which ultimately results in improved outcomes for patients. Magnetic resonance imaging (MRI) revolutionized diagnostic imaging with its superior soft tissue contrast, high spatiotemporal resolution, and freedom from ionizing radiation exposure. Over the past few years there has been burgeoning interest in MR-guided radiotherapy (MRgRT) to overcome current challenges in X-ray-based IGRT, including but not limited to, suboptimal soft tissue contrast, lack of efficient daily adaptation, and incremental exposure to ionizing radiation. In this review, we present an overview of the technologic advancements in IGRT that led to MRI-linear accelerator (MRL) integration. Our report is organized in three parts: (1) a historical timeline tracing the origins of radiotherapy and evolution of IGRT, (2) currently available MRL technology, and (3) future directions and aspirations for MRL applications.

Indexed as

adaptive therapyIGRTimage-guided radiotherapylinear acceleratormagnetic resonanceMRgRTradiotherapy

Identifiers

PMID36012969
PMCPMC9409689

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.