Evidence map›Paper›PMID 37521357›Full record

ArticleFrontiers in medicine2023

A simulation study of in-beam visualization system for proton therapy by monitoring scattered protons.

Shogo Sato, Hiromu Yokokawa, Mana Hosobuchi, Jun Kataoka

Abstract read
In one paragraph

Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Recent advances in applying machine learning to proton radiotherapy.Biomedical physics & engineering express · 2025
    Pooled it
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

4 authors.

Shogo SatoFaculty of Science and Engineering, Waseda University, Tokyo, Japan.
Hiromu YokokawaFaculty of Science and Engineering, Waseda University, Tokyo, Japan.
Mana HosobuchiFaculty of Science and Engineering, Waseda University, Tokyo, Japan.
Jun KataokaFaculty of Science and Engineering, Waseda University, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, in-beam positron emission tomography (PET) has been actively researched for reducing biological washout effects and dose monitoring during irradiation. However, the positron distribution does not precisely reflect the dose distribution since positron production and ionization are completely different physical processes. Thus, a novel in-beam system was proposed to determine proton dose range by measuring scattered protons with dozens of scintillation detectors surrounding the body surface. While previous studies conducted a preliminary experiment with a simple phantom, we simulated more complex situations in this paper. Especially, we conducted three stepwise simulation studies to demonstrate the feasibility of the proposed method. First, a simple rectangular phantom was reproduced on simulation and irradiated with protons for obtaining current values and Monte Carlo (MC) dose. Next, we trained a deep learning model to estimate 2-dimensional-dose range (2D-DL dose) from measured current values for simulation (A). We simulated plastic scintillators as detectors to measure the scattered protons. Second, a rectangular phantom with an air layer was used, and 3D-DL dose was estimated in simulation (B). Finally, a cylindrical phantom that mimics the human body was used for confirming the estimation quality of the simulation (C). Consequently, the position of the Bragg peak was estimated with an error of 1.0 mm in simulation (A). In addition, the position of the air layer, as well as the verifying peak position with an error of 2.1 mm, was successfully estimated in simulation (B). Although the estimation error of the peak position was 12.6 mm in simulation (C), the quality was successfully further improved to 9.3 mm by incorporating the mass density distribution obtained from the computed tomography (CT). These simulation results demonstrated the potential of the as-proposed verification system. Additionally, the effectiveness of CT utilization for estimating the DL dose was also indicated.

Indexed as

CT utilizationcurrent readoutdeep learningdose range verificationproton therapyscattering protonsscintillation detectors

Identifiers

PMID37521357
PMCPMC10375415

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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.