Evidence map›Paper›PMID 38373657›Full record

ArticleInternational journal of radiation oncology, biology, physics2024

Clinical Experience With an Offline Adaptive Radiation Therapy Head and Neck Program: Dosimetric Benefits and Opportunities for Patient Selection.

Michalis Aristophanous, Eric Aliotta, Phillip Lichtenwalner, Shira Abraham, Mohammad Nehmeh, Amanda Caringi, Peng Zhang, Yu-Chi Hu, Pengpeng Zhang, Laura Cervino and 7 more

Open access · greenAbstract read
In one paragraph

Article in International journal of radiation oncology, biology, physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
7.5field-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

11 citing papers in PubMed, 13 citations in OpenAlex.

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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

17 authors at 2 institutions in 1 country.

Michalis AristophanousDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York. Electronic address: aristopm@mskcc.org.
Eric AliottaDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Phillip LichtenwalnerDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Shira AbrahamDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Mohammad NehmehDepartment of Applied Physics, Columbia University, New York, New York.
Amanda CaringiDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Peng ZhangDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Yu-Chi HuDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Pengpeng ZhangDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Laura CervinoDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Daphna GelblumDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
Sean McBrideDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
Nadeem RiazDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
Linda ChenDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
Yao YuDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
Kaveh ZakeriDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
Nancy LeeDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
Memorial Sloan Kettering Cancer Center · USColumbia University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

purposeThe objective of this study was to develop a linear accelerator (LINAC)-based adaptive radiation therapy (ART) workflow for the head and neck that is informed by automated image tracking to identify major anatomic changes warranting adaptation. In this study, we report our initial clinical experience with the program and an investigation into potential trigger signals for ART. METHODS AND MATERIALS: Offline ART was systematically performed on patients receiving radiation therapy for head and neck cancer on C-arm LINACs. Adaptations were performed at a single time point during treatment with resimulation approximately 3 weeks into treatment. Throughout treatment, all patients were tracked using an automated image tracking system called the Automated Watchdog for Adaptive Radiotherapy Environment (AWARE). AWARE measures volumetric changes in gross tumor volumes (GTVs) and selected normal tissues via cone beam computed tomography scans and deformable registration. The benefit of ART was determined by comparing adaptive plan dosimetry and normal tissue complication probabilities against the initial plans recalculated on resimulation computed tomography scans. Dosimetric differences were then correlated with AWARE-measured volume changes to identify patient-specific triggers for ART. Candidate trigger variables were evaluated using receiver operator characteristic analysis.

resultsIn total, 46 patients received ART in this study. Among these patients, we observed a significant decrease in dose to the submandibular glands (mean ± standard deviation: -219.2 ± 291.2 cGy, P < 10

conclusionsSystematic offline head and neck ART was successfully deployed on conventional LINACs and reduced doses to critical salivary structures and the oral cavity. Automated cone beam computed tomography tracking provided information regarding anatomic changes that may aid patient-specific triggering for ART.

Indexed as

Cone-Beam Computed TomographyHead and Neck NeoplasmsOrgans at RiskPatient SelectionRadiotherapy Planning, Computer-AssistedTumor BurdenAgedFemaleHumansMaleMiddle AgedParotid GlandRadiotherapy DosageRadiotherapy, Image-GuidedWorkflow

Identifiers

PMID38373657
PMCPMC11636647
OpenAlexW4391915051

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.