Evidence map›Paper›PMID 42174043›Full record

ArticleScientific reports2026

Neural network modelling of proton RBE values at predominant survival fractions of in vitro data.

Erlend Lyngholm, Armin Lühr, Liheng Tian, Camilla Hanquist Stokkevåg, Helge Henjum, Andreas Havsgård Handeland, Johannes Tjelta, Kristian Smeland Ytre-Hauge

Abstract read
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In one paragraph

Article in Scientific reports, 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

8 authors.

Erlend LyngholmDepartment of Physics and Technology, University of Bergen, Bergen, Norway. erlend.lyngholm@uib.no.
Armin LührDepartment of Physics, TU Dortmund University, Dortmund, Germany.
Liheng TianDepartment of Physics, TU Dortmund University, Dortmund, Germany.
Camilla Hanquist StokkevågDepartment of Physics and Technology, University of Bergen, Bergen, Norway.
Helge HenjumDepartment of Physics and Technology, University of Bergen, Bergen, Norway.
Andreas Havsgård HandelandDepartment of Physics and Technology, University of Bergen, Bergen, Norway.
Johannes TjeltaDepartment of Physics and Technology, University of Bergen, Bergen, Norway.
Kristian Smeland Ytre-HaugeDepartment of Physics and Technology, University of Bergen, Bergen, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While a constant relative biological effectiveness (RBE) of 1.1 is applied in clinical proton therapy, longstanding clinical concern and in vitro evidence demonstrate a variable proton RBE. Several RBE models for protons have been developed based on general assumptions of RBE dependencies together with fitting to in vitro data, for which some modelling authors have included greater diversity of cell types than others. In this study we use neural networks (NNs) to model the proton RBE without any predefined dependencies, investigate the effect of using different biological inputs and combinations of input parameters in the modelling, and compare the results to previous approaches. A comprehensive in vitro RBE database of 431 data points was used, including parameter values for the proton linear energy transfer (LET), the (α/β)

Indexed as

Neural Networks, ComputerProtonsProton TherapyRelative Biological EffectivenessCell SurvivalHumansLinear Energy TransferModels, BiologicalProtons

Identifiers

PMID42174043
PMCPMC13408797

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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.