Evidence map›Paper›PMID 26167202›Full record

ArticleComputational and mathematical methods in medicine2015

Nanodosimetry-Based Plan Optimization for Particle Therapy.

Margherita Casiraghi, Reinhard W Schulte

Open access · hybridAbstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2015. 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
1.4field-weighted citation impact, top 18% of its field
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, 23 citations in OpenAlex.

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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

2 authors at 2 institutions in 2 countries.

Margherita CasiraghiCenter for Proton Therapy, Paul Scherrer Institut (PSI), 5232 Villigen, Switzerland.
Reinhard W SchulteDivision of Radiation Research, Loma Linda University, Loma Linda, CA 92350, USA.
Loma Linda University · USPaul Scherrer Institute · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment planning for particle therapy is currently an active field of research due uncertainty in how to modify physical dose in order to create a uniform biological dose response in the target. A novel treatment plan optimization strategy based on measurable nanodosimetric quantities rather than biophysical models is proposed in this work. Simplified proton and carbon treatment plans were simulated in a water phantom to investigate the optimization feasibility. Track structures of the mixed radiation field produced at different depths in the target volume were simulated with Geant4-DNA and nanodosimetric descriptors were calculated. The fluences of the treatment field pencil beams were optimized in order to create a mixed field with equal nanodosimetric descriptors at each of the multiple positions in spread-out particle Bragg peaks. For both proton and carbon ion plans, a uniform spatial distribution of nanodosimetric descriptors could be obtained by optimizing opposing-field but not single-field plans. The results obtained indicate that uniform nanodosimetrically weighted plans, which may also be radiobiologically uniform, can be obtained with this approach. Future investigations need to demonstrate that this approach is also feasible for more complicated beam arrangements and that it leads to biologically uniform response in tumor cells and tissues.

Indexed as

IonsProton TherapyAlgorithmsBiophysicsCarbonComputer SimulationDNA DamageHumansModels, StatisticalMonte Carlo MethodNeoplasmsNormal DistributionPhantoms, ImagingRadiometryRadiotherapy DosageRadiotherapy Planning, Computer-AssistedCarbonIons

Identifiers

PMID26167202
PMCPMC4475762
OpenAlexW1588626184

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.