Evidence map›Paper›PMID 41313446›Full record

ArticleStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2026

Automated quality assurance of imaging dose and protocol adherence in computed tomography radiotherapy planning using TotalSegmentator-based segmentation.

Niklas A Lackner, Andre Karius, Tobias Brandt, Oliver J Ott, Florian Putz, Vratislav Strnad, Matthias S May, Rainer Fietkau, Christoph Bert, Juliane Szkitsak

Abstract read
In one paragraph

Article in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Niklas A LacknerDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstr. 27, 91054, Erlangen, Germany. niklas.lackner@uk-erlangen.de.ORCID 0009-0001-1994-9975
Andre KariusDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstr. 27, 91054, Erlangen, Germany.ORCID 0000-0002-3842-1375
Tobias BrandtDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstr. 27, 91054, Erlangen, Germany.
Oliver J OttDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstr. 27, 91054, Erlangen, Germany.ORCID 0000-0001-5806-6158
Florian PutzDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstr. 27, 91054, Erlangen, Germany.ORCID 0000-0003-3966-2872
Vratislav StrnadDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstr. 27, 91054, Erlangen, Germany.ORCID 0000-0002-3871-7563
Matthias S MayInstitute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-University (FAU), Erlangen, Germany.ORCID 0000-0002-2540-850X
Rainer FietkauDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstr. 27, 91054, Erlangen, Germany.
Christoph BertDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstr. 27, 91054, Erlangen, Germany.ORCID 0000-0002-8539-6600
Juliane SzkitsakDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstr. 27, 91054, Erlangen, Germany.ORCID 0000-0003-4603-492X

Funding

NUM 2.0 FKZ 01KX2121NUM 3.0 FKZ: 01KX2524
6 · The paper itself

Abstract

purposeComputed tomography (CT) scans are vital for radiotherapy planning, providing essential data for dose calculations. This study retrospectively evaluated imaging doses, scan lengths, and protocol adherence to support imaging optimization and reduce patient radiation exposure.

methodsCT data from patients undergoing external beam radiotherapy and brachytherapy in the period 04/2021 to 12/2024 were retrieved from the institutional picture archiving and communication system (PACS). Imaging doses (volumetric CT dose index [CTDIvol] and dose length product [DLP]) were extracted from dose reports. Automated organ segmentation was used to assess standard operating procedures (SOPs) adherence by estimating anatomical scan length differences. Additional quality assurance checks assessed protocol and imaging consistency.

resultsBrain protocols exhibited the highest CTDIvol (73 ± 12 mGy), while head and neck protocols had higher DLP values (3212 ± 757 mGy·cm). The lung 4D protocol showed a higher effective dose (23 ± 9 mSv) compared to the standard lung protocol. Notable anatomical scan length differences were observed at the lower boundary in the upper abdomen (120 ± 75 mm) and spine (155 ± 159 mm), indicating opportunities for workflow improvement.

conclusionEnhancing CT workflows for radiotherapy patients is important and feasible. Dose and scan length analyses suggest that revising institutional SOPs, optimizing X‑ray tube modulation, and refining scan length boundaries should be considered to achieve this goal.

Indexed as

Guideline AdherenceQuality Assurance, Health CareRadiation DosageRadiotherapy Planning, Computer-AssistedTomography, X-Ray ComputedBrachytherapyHumansRadiotherapy DosageRetrospective StudiesComputed-tomography dose optimizationDeep learningRadiation exposureRetrospective analysisWorkflow optimization

Identifiers

PMID41313446
PMCPMC12953283

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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.