Evidence map›Paper›PMID 37497188›Full record

ArticlePhysics and imaging in radiation oncology2023

Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen.

Riccardo Dal Bello, Mariia Lapaeva, Agustina La Greca Saint-Esteven, Philipp Wallimann, Manuel Günther, Ender Konukoglu, Nicolaus Andratschke, Matthias Guckenberger, Stephanie Tanadini-Lang

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Article in Physics and imaging in radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

9 authors.

Riccardo Dal BelloDepartment of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Mariia LapaevaDepartment of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Agustina La Greca Saint-EstevenDepartment of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Philipp WallimannDepartment of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Manuel GüntherArtificial Intelligence and Machine Learning Group, Department of Informatics, University of Zurich, Zurich, Switzerland.
Ender KonukogluComputer Vision Laboratory, ETH Zurich, Zurich, Switzerland.
Nicolaus AndratschkeDepartment of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Matthias GuckenbergerDepartment of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Stephanie Tanadini-LangDepartment of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and purpose: The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT) led to an increasing interest towards MR-only radiotherapy. For the latter, the dose calculation should be performed on a synthetic CT (sCT). Patient-specific quality assurance (PSQA) methods have not been established yet and this study aimed to assess several software-based solutions. Materials and methods: A retrospective study was performed on 20 patients treated at an MR-Linac, which were selected to evenly cover four subcategories: (i) standard, (ii) air pockets, (iii) lung and (iv) implant cases. The neural network (NN) CycleGAN was adopted to generate a reference sCT, which was then compared to four PSQA methods: (A) water override of body, (B) five tissue classes with bulk densities, (C) sCT generated by a separate NN (pix2pix) and (D) deformed CT. Results: The evaluation of the dose endpoints demonstrated that while all methods A-D provided statistically equivalent results (p = 0.05) within the 2% level for the standard cases (i), only the methods C-D guaranteed the same result over the whole cohort. The bulk densities override was shown to be a valuable method in absence of lung tissue within the beam path. Conclusion: The observations of this study suggested that the use of an additional sCT generated by a separate NN was an appropriate tool to perform PSQA of a sCT in an MR-only workflow at an MR-Linac. The time and dose endpoints requirements were respected, namely within 10 min and 2%.

Indexed as

MR-guided radiotherapyMR-LinacNeural networkPSQAQuality assurancesynthetic CT

Identifiers

PMID37497188
PMCPMC10366576

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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.