Evidence map›Paper›PMID 38896835›Full record

ArticleJournal of applied clinical medical physics2024

MU variability in CBCT-guided online adaptive radiation therapy.

Sean Tanny, Olga M Dona Lemus, Joshua Wancura, Nicholas Sperling, Matthew Webster, Hyunuk Jung, Yuwei Zhou, Fiona Li, Jihyung Yoon, Alexander Podgorsak and 1 more

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. MU variability in CBCT-guided online adaptive radiation therapy.Journal of applied clinical medical physics · 2024
    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

11 authors.

Sean TannyDepartment of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.ORCID https://orcid.org/0000-0002-0915-9187
Olga M Dona LemusDepartment of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
Joshua WancuraDepartment of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
Nicholas SperlingDepartment of Radiation Oncology, University of Toledo Medical Center, Toledo, Ohio, USA.ORCID https://orcid.org/0000-0001-6851-492X
Matthew WebsterDepartment of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
Hyunuk JungDepartment of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
Yuwei ZhouDepartment of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
Fiona LiDepartment of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
Jihyung YoonDepartment of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
Alexander PodgorsakDepartment of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.ORCID https://orcid.org/0000-0001-6351-703X
Dandan ZhengDepartment of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeCBCT-guided online-adaptive radiotherapy (oART) systems have been made possible by using artificial intelligence and automation to substantially reduce treatment planning time during on-couch adaptive sessions. Evaluating plans generated during an adaptive session presents significant challenges to the clinical team as the planning process gets compressed into a shorter window than offline planning. We identified MU variations up to 30% difference between the adaptive plan and the reference plan in several oART sessions that caused the clinical team to question the accuracy of the oART dose calculation. We investigated the cause of MU variation and the overall accuracy of the dose delivered when MU variations appear unnecessarily large.

methodsDosimetric and adaptive plan data from 604 adaptive sessions of 19 patients undergoing CBCT-guided oART were collected. The analysis included total MU per fraction, planning target volume (PTV) and organs at risk (OAR) volumes, changes in PTV-OAR overlap, and DVH curves. Sessions with MU greater than two standard deviations from the mean were reoptimized offline, verified by an independent calculation system, and measured using a detector array.

resultsMU variations relative to the reference plan were normally distributed with a mean of -1.0% and a standard deviation of 11.0%. No significant correlation was found between MU variation and anatomic changes. Offline reoptimization did not reliably reproduce either reference or on-couch total MUs, suggesting that stochastic effects within the oART optimizer are likely causing the variations. Independent dose calculation and detector array measurements resulted in acceptable agreement with the planned dose.

conclusionsMU variations observed between oART plans were not caused by any errors within the oART workflow. Providers should refrain from using MU variability as a way to express their confidence in the treatment planning accuracy. Clinical decisions during on-couch adaptive sessions should rely on validated secondary dose calculations to ensure optimal plan selection.

Indexed as

Cone-Beam Computed TomographyOrgans at RiskRadiotherapy DosageRadiotherapy, Image-GuidedRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedAlgorithmsHumansImage Processing, Computer-AssistedNeoplasmsadaptive radiotherapyCBCT‐based adaptive RTethos‐based adaptiveMU variabilitypatient specific QAplan QA

Identifiers

PMID38896835
PMCPMC11492364

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Registered trials

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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.