Evidence map›Paper›PMID 42212206›Full record

ArticlePhysics and imaging in radiation oncology2026

Deep learning for dose-averaged linear energy transfer estimation in pencil-beam scanning and double scattering proton radiotherapy plans with uncertainty-aware external validation.

Aaron Kieslich, Yerik Singh, Martina Palkowitsch, Sebastian Starke, Fabian Hennings, Esther G C Troost, Mechthild Krause, Jona Bensberg, Armin Lühr, Feline Heinzelmann and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Physics and imaging in radiation oncology, 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

15 authors.

Aaron KieslichOncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
Yerik SinghOncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
Martina PalkowitschOncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
Sebastian StarkeOncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
Fabian HenningsOncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
Esther G C TroostOncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
Mechthild KrauseOncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
Jona BensbergTU Dortmund University, Department of Physics, Dortmund, Germany.
Armin LührTU Dortmund University, Department of Physics, Dortmund, Germany.
Feline HeinzelmannWest German Proton Therapy Center Essen (WPE), University Hospital Essen, Essen, Germany.
Christian BäumerTU Dortmund University, Department of Physics, Dortmund, Germany.
Beate TimmermannWest German Proton Therapy Center Essen (WPE), University Hospital Essen, Essen, Germany.
Nicolas DepauwDepartment of Radiation Oncology, Massachusetts General Hospital/Mass General Brigham and Harvard Medical School, Boston, MA, United States.
Helen A ShihDepartment of Radiation Oncology, Massachusetts General Hospital/Mass General Brigham and Harvard Medical School, Boston, MA, United States.
Steffen LöckOncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Purpose: Accounting for the linear energy transfer (LET) in proton radiotherapy may reduce treatment-related side effects. When Monte Carlo (MC) simulations are unavailable, deep-learning (DL) surrogate models can be applied. We develop DL LET models for brain tumour patients and assess their uncertainty for an external dataset lacking LET reference. Material and Methods: A multi-institutional dataset of 570 patients with 605 treatment plans was used for model development and evaluation. DL models predicting dose-averaged LET (LET Results: The PBS+DS model achieved average median VAEs below 0.42 keV/ Conclusion: DL models accurately approximate LET

Indexed as

Deep learningDouble scatteringLinear energy transferPencil beam scanningProton radiotherapyUncertainty quantification

Identifiers

PMID42212206
PMCPMC13213858

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.