Evidence map›Paper›PMID 42422140›Full record

ArticleJournal of medical physics

A Digital Imaging and Communications in Medicine-based Collision Prediction System with an Angle Optimization Algorithm for Electron Beam Therapy.

Hideharu Miura, Toshiya Okazue

Abstract read
In one paragraph

Article in Journal of medical physics. 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

2 authors.

Hideharu MiuraHiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan.
Toshiya OkazueHiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The purpose of this study was to develop and validate a Digital Imaging and Communications in Medicine (DICOM)-based collision prediction system for electron beam therapy. This system enables a quantitative pretreatment assessment of mechanical interference between the electron applicator and the patient's body surface. It also automatically proposes safe alternative beam angle combinations. Materials and Methods: We geometrically approximated a 20 cm × 20 cm electron applicator mounted on a TrueBeam STx linear accelerator as a 190-mm cube. We extracted body and table contours, as well as isocenter coordinates, from the RT Structure Set. For each combination of gantry, table, and collimator angles, the minimum clearance distance between the applicator model and the body contour was calculated. The discretized angle space was visualized using heatmaps of the minimum clearance distance. An optimization algorithm was implemented to calculate a score for each candidate angle set using a weighted sum of angular deviations from the original plan and to identify the best alternative angle configurations. Results: The system was evaluated using an adult female anthropomorphic phantom and four intentionally selected electron regions (superior, inferior, medial, and lateral around the left nipple). For the superior, inferior, and medial regions, the initial planned angles produced geometric intersections. However, the system-suggested alternative angle sets eliminated these intersections in three-dimensional visualization and were confirmed to be collision-free on the linear accelerator. Conclusions: The proposed system requires no additional hardware, integrates with current workflows, and provides a practical tool for collision risk assessment and angle optimization in nearsurface electron therapy.

Indexed as

Clearance mappingcollision predictionDigital Imaging and Communications in Medicineelectron beam therapy

Identifiers

PMID42422140
PMCPMC13344924

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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.