Evidence map›Paper›PMID 37660057›Full record

ReviewRadiation oncology (London, England)2023

Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction.

Hefei Liu, David Schaal, Heather Curry, Ryan Clark, Anthony Magliari, Patrick Kupelian, Deepak Khuntia, Sushil Beriwal

Abstract readReview
In one paragraph

Review in Radiation oncology (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.

0numbers the graph read from it
0cells of the map it votes in
72citing 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

72 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

12 more citing papers are in PubMed but not listed here.

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

8 authors.

Hefei LiuDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, USA.
David SchaalVarian Medical Systems Inc, Palo Alto, CA, USA.
Heather CurryVarian Medical Systems Inc, Palo Alto, CA, USA.
Ryan ClarkVarian Medical Systems Inc, Palo Alto, CA, USA.
Anthony MagliariVarian Medical Systems Inc, Palo Alto, CA, USA.
Patrick KupelianVarian Medical Systems Inc, Palo Alto, CA, USA.
Deepak KhuntiaVarian Medical Systems Inc, Palo Alto, CA, USA.
Sushil BeriwalVarian Medical Systems Inc, Palo Alto, CA, USA. sushilberiwal@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adaptive radiotherapy (ART) was introduced in the late 1990s to improve the accuracy and efficiency of therapy and minimize radiation-induced toxicities. ART combines multiple tools for imaging, assessing the need for adaptation, treatment planning, quality assurance, and has been utilized to monitor inter- or intra-fraction anatomical variations of the target and organs-at-risk (OARs). Ethos™ (Varian Medical Systems, Palo Alto, CA), a cone beam computed tomography (CBCT) based radiotherapy treatment system that uses artificial intelligence (AI) and machine learning to perform ART, was introduced in 2020. Since then, numerous studies have been done to examine the potential benefits of Ethos™ CBCT-guided ART compared to non-adaptive radiotherapy. This review will explore the current trends of Ethos™, including improved CBCT image quality, a feasible clinical workflow, daily automated contouring and treatment planning, and motion management. Nevertheless, evidence of clinical improvements with the use of Ethos™ are limited and is currently under investigation via clinical trials.

Indexed as

Radiation InjuriesRadiation OncologyArtificial IntelligenceCone-Beam Computed TomographyHumansMachine LearningMotionAdaptive radiotherapy (ART)Cone-beam computed tomography (CBCT)Ethos™

Identifiers

PMID37660057
PMCPMC10475190

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.