Evidence map›Paper›PMID 38368614›Full record

ArticleJournal of applied clinical medical physics2024

A retrospective study on the investigation of potential dosimetric benefits of online adaptive proton therapy for head and neck cancer.

Chih-Wei Chang, Duncan Bohannon, Zhen Tian, Yinan Wang, Mark W Mcdonald, David S Yu, Tian Liu, Jun Zhou, Xiaofeng Yang

Open access · goldAbstract 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.4field-weighted citation impact, top 14% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

9 authors at 3 institutions in 1 country.

Chih-Wei ChangDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.ORCID https://orcid.org/0000-0002-3818-4381
Duncan BohannonDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Zhen TianDepartment of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois, USA.
Yinan WangDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Mark W McdonaldDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
David S YuDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Tian LiuDepartment of Radiation Oncology, Mount Sinai Medical Center, New York, New York, USA.
Jun ZhouDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.ORCID https://orcid.org/0000-0002-6078-9424
Xiaofeng YangDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Emory University · USMount Sinai Medical Center · USUniversity of Chicago · US

Funding

Real-time Volumetric Imaging for Motion Management and Dose Delivery VerificationR01CA272991 · NCI · EMORY UNIVERSITY · PI Zhen Tian, Xiaofeng Yang · 2023 to 2026
$2.3M
Artificial Intelligence Driven Automatic Treatment Planning of Stereotactic Radiosurgery for the Management of Multiple Brain MetastasesR37CA272755 · NCI · UNIVERSITY OF CHICAGO · PI Zhen Tian · 2022 to 2026
$1.8M
NCI NIH HHS R01 CA272991NCI NIH HHS R37 CA272755NIH HHS R01CA272991NIH HHS R37CA272755
6 · The paper itself

Abstract

purposeProton therapy is sensitive to anatomical changes, often occurring in head-and-neck (HN) cancer patients. Although multiple studies have proposed online adaptive proton therapy (APT), there is still a concern in the radiotherapy community about the necessity of online APT. We have performed a retrospective study to investigate the potential dosimetric benefits of online APT for HN patients relative to the current offline APT.

methodsOur retrospective study has a patient cohort of 10 cases. To mimic online APT, we re-evaluated the dose of the in-use treatment plan on patients' actual treatment anatomy captured by cone-beam CT (CBCT) for each fraction and performed a templated-based automatic replanning if needed, assuming that these were performed online before treatment delivery. Cumulative dose of the simulated online APT course was calculated and compared with that of the actual offline APT course and the designed plan dose of the initial treatment plan (referred to as nominal plan). The ProKnow scoring system was employed and adapted for our study to quantify the actual quality of both courses against our planning goals.

resultsThe average score of the nominal plans over the 10 cases is 41.0, while those of the actual offline APT course and our simulated online course is 25.8 and 37.5, respectively. Compared to the offline APT course, our online course improved dose quality for all cases, with the score improvement ranging from 0.4 to 26.9 and an average improvement of 11.7.

conclusionThe results of our retrospective study have demonstrated that online APT can better address anatomical changes for HN cancer patients than the current offline replanning practice. The advanced artificial intelligence based automatic replanning technology presents a promising avenue for extending potential benefits of online APT.

Indexed as

Head and Neck NeoplasmsOrgans at RiskProton TherapyRadiotherapy DosageRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedCone-Beam Computed TomographyHumansPrognosisRetrospective Studieshead‐and‐neck canceroffline adaptive proton therapyonline adaptive proton therapytreatment outcome

Identifiers

PMID38368614
PMCPMC11087169
OpenAlexW4391921946

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.