Evidence map›Paper›PMID 37354344›Full record

ArticleJapanese journal of radiology2023

A new workflow of the on-line 1.5-T MR-guided adaptive radiation therapy.

Takashi Uno, Masato Tsuneda, Kota Abe, Yukio Fujita, Rintaro Harada, Makoto Saito, Aki Kanazawa, Asuka Kodate, Yukinao Abe, Yohei Ikeda and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Japanese journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.2field-weighted citation impact, top 8% 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

8 citing papers in PubMed, 9 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

12 authors at 2 institutions in 1 country.

Takashi UnoDiagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan. unotakas@faculty.chiba-u.jp.ORCID http://orcid.org/0000-0003-4606-5662
Masato TsunedaDepartment of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Kota AbeDepartment of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Yukio FujitaDepartment of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Rintaro HaradaDepartment of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Makoto SaitoDepartment of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Aki KanazawaDepartment of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Asuka KodateDepartment of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Yukinao AbeDepartment of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Yohei IkedaDepartment of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Miho Watanabe NemotoDiagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Hajime YokotaDiagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuou-ku, Chiba City, Chiba, 260-8670, Japan.
Chiba University · JPChiba University Hospital · JP

Funding

JSPS KAKENHI 22K07661
6 · The paper itself

Abstract

purposeThe aim of this study was to develop a new workflow for 1.5-T magnetic resonance (MR)-guided on-line adaptive radiation therapy (MRgART) and assess its feasibility in achieving dose constraints. MATERIALS AND

methodsWe retrospectively evaluated the clinical data of patients who underwent on-line adaptive radiation therapy using a 1.5-T MR linear accelerator (MR-Linac). The workflow in MRgART was established by reviewing the disease site, number of fractions, and re-planning procedures. Five cases of prostate cancer were selected to evaluate the feasibility of the new workflow with respect to achieving dose constraints.

resultsBetween December 2021 and September 2022, 50 consecutive patients underwent MRgART using a 1.5-T MR-Linac. Of these, 20 had prostate cancer, 10 had hepatocellular carcinoma, 6 had pancreatic cancer, 5 had lymph node oligo-metastasis, 3 had renal cancer, 3 had bone metastasis, 2 had liver metastasis from colon cancer, and 1 had a mediastinal tumor. Among a total of 247 fractions, 235 (95%) were adapt-to-shape (ATS)-based re-planning. The median ATS re-planning time in all 50 cases was 17 min. In the feasibility study, all dose constraint sets were met in all 5 patients by ATS re-planning. Conversely, a total of 14 dose constraints in 5 patients could not be achieved by virtual plan without using adaptive re-planning. These dose constraints included the minimum dose received by the highest irradiated volume of 1 cc in the planning target volume and the maximum dose of the rectal/bladder wall.

conclusionA new workflow of 1.5-T MRgART was established and found to be feasible. Our evaluation of the dose constraint achievement demonstrated the effectiveness of the workflow.

Indexed as

Prostatic NeoplasmsRadiotherapy Planning, Computer-AssistedHumansMagnetic Resonance SpectroscopyMaleRadiotherapy DosageRetrospective StudiesWorkflowAdaptive radiation therapyAdapt to shapeMRgARTMR-LinacWorkflow

Identifiers

PMID37354344
PMCPMC10613593
OpenAlexW4381889722

What OpenQuestion holds

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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.