Evidence map›Paper›PMID 40248771›Full record

ArticlePhysics and imaging in radiation oncology2025

Clinical implementation of patient-specific quality assurance for synthetic computed tomography.

Francesca Nella, Stephanie Tanadini-Lang, Riccardo Dal Bello

Abstract read
In one paragraph

Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Francesca NellaDepartment 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.
Riccardo Dal BelloDepartment 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: In a magnetic resonance (MR) only planning workflow, MR image is the sole dataset acquired. In order to calculate the dose deposition, a synthetic CT (sCT) is generated to substitute the planning computed tomography (CT). This study aimed to establish acceptance criteria for the clinical implementation of patient-specific quality assurance (PSQA) for sCT. Materials and methods: A retrospective study was conducted on 60. 30 patients underwent a CT scan in treatment position and an MR in diagnostic position. 30 patients had both CT and MR images acquired in treatment position. For the latter group, a sCT for dose calculation was generated and compared against three PSQA methods: recalculation on (A) water override of the body, (B) tissue classes with bulk density overrides and (C) planning CT. The relative dose differences (ΔD [%]) between the sCT and the PSQA methos were evaluated. Results: ΔD for PTV Dmean for method (A) were within 3% for pelvis and 4% for brain cohorts, with standard deviations below 1%. Methods (B) and (C) remained within 2% and 1%, respectively, with deviations up to 1%. Conclusion: The present study proposes a robust PSQA method for MR-only planning. Method (A) is a valuable tool for identifying potential large outliers for Dmean deviations (> 5 %) and it is proposed as the routine PSQA. Method (B) can be used for pelvis cases to improve detection to the 2 % level if method (A) fails. If both (A) and (B) fail, method (C) can be used as a fall-back.

Indexed as

Acceptance criteriaBrainClinical implementationCommissioningMR-onlyPelvisPSQAQuality assuranceSynthetic CT

Identifiers

PMID40248771
PMCPMC12005318

What OpenQuestion holds

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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.