Evidence map›Paper›PMID 35396707›Full record

ArticleMedical physics2022

Image guidance for FLASH radiotherapy.

Issam El Naqa, Brian W Pogue, Rongxiao Zhang, Ibrahim Oraiqat, Katia Parodi

Erratum issuedOpen 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. An erratum has been issued. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
7.7field-weighted citation impact, top 2% 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

23 citing papers in PubMed, 34 citations in OpenAlex.

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  13. 4DPhysics in medicine and biology · 2024
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  20. Emerging technologies for cancer therapy using accelerated particles.Progress in particle and nuclear physics · 2023
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Issam El NaqaDepartment of Machine Learning, Moffitt Cancer Center, Tampa, Florida, USA.
Brian W PogueThayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA.
Rongxiao ZhangGiesel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA.
Ibrahim OraiqatDepartment of Machine Learning, Moffitt Cancer Center, Tampa, Florida, USA.
Katia ParodiDepartment of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching, Germany.
Dartmouth College · USMoffitt Cancer Center · USLudwig-Maximilians-Universität München · DE

Funding

Combined radiation acoustics and ultrasound imaging for real-time guidance in radiotherapyR37CA222215 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EL NAQA, ISSAM M. · 2018 to 2024
$3.5M
An Ionizing Radiation Acoustics Imaging (iRAI) Approach for guided Flash RadiotherapyR01CA266803 · NCI · H. LEE MOFFITT CANCER CTR & RES INST · PI BORTFELD, THOMAS R., EL NAQA, ISSAM M. · 2022 to 2025
$3.3M
Cerenkov Multi-Spectral Imaging (CMSI) for Adaptation and Real-Time Imaging in RadiotherapyR41CA243722 · NCI · ENDECTRA, LLC · PI EL NAQA, ISSAM M. · 2020 to 2020
$327k
NCI NIH HHS R01 CA266803NCI NIH HHS R37 CA222215NCI NIH HHS R41 CA243722
6 · The paper itself

Abstract

FLASH radiotherapy (FLASH-RT) is an emerging ultra-high dose (>40 Gy/s) delivery that promises to improve the therapeutic potential by limiting toxicities compared to conventional RT while maintaining similar tumor eradication efficacy. Image guidance is an essential component of modern RT that should be harnessed to meet the special emerging needs of FLASH-RT and its associated high risks in planning and delivering of such ultra-high doses in short period of times. Hence, this contribution will elaborate on the imaging requirements and possible solutions in the entire chain of FLASH-RT treatment, from the planning, through the setup and delivery with online in vivo imaging and dosimetry, up to the assessment of biological mechanisms and treatment response. In patient setup and delivery, higher temporal sampling than in conventional RT should ensure that the short treatment is delivered precisely to the targeted region. Additionally, conventional imaging tools such as cone-beam computed tomography will continue to play an important role in improving patient setup prior to delivery, while techniques based on magnetic resonance imaging or positron emission tomography may be extremely valuable for either linear accelerator (Linac) or particle FLASH therapy, to monitor and track anatomical changes during delivery. In either planning or assessing outcomes, quantitative functional imaging could supplement conventional imaging for more accurate utilization of the biological window of the FLASH effect, selecting for or verifying things such as tissue oxygen and existing or transient hypoxia on the relevant timescales of FLASH-RT delivery. Perhaps most importantly at this time, these tools might help improve the understanding of the biological mechanisms of FLASH-RT response in tumor and normal tissues. The high dose deposition of FLASH provides an opportunity to utilize pulse-to-pulse imaging tools such as Cherenkov or radiation acoustic emission imaging. These could provide individual pulse mapping or assessing the 3D dose delivery superficially or at tissue depth, respectively. In summary, the most promising components of modern RT should be used for safer application of FLASH-RT, and new promising developments could be advanced to cope with its novel demands but also exploit new opportunities in connection with the unique nature of pulsed delivery at unprecedented dose rates, opening a new era of biological image guidance and ultrafast, pulse-based in vivo dosimetry.

Indexed as

NeoplasmsTomography, X-Ray ComputedHumansParticle AcceleratorsPositron-Emission TomographyRadiotherapyRadiotherapy Dosagebiological image guidanceFLASH-RTin vivo dosimetryonline imaging

Identifiers

PMID35396707
PMCPMC9844128
OpenAlexW4223510974

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.