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Record W4392598583 · doi:10.1002/mp.17017

A comprehensive quality assurance protocol for electromagnetic tracking in brachytherapy

2024· article· en· W4392598583 on OpenAlexaff
Christopher Dürrbeck, Isaac Neri Gomez‐Sarmiento, Ioannis Androulakis, Birte Christina Sauer, Inger‐Karine K. Kolkman‐Deurloo, Christoph Bert, Luc Beaulieu

Bibliographic record

VenueMedical Physics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
FundersFriedrich-Alexander-Universität Erlangen-NürnbergElekta
KeywordsQuality assuranceImaging phantomJitterComputer scienceProtocol (science)BrachytherapyTracking (education)DosimetryGenerator (circuit theory)Medical physicsSimulationReal-time computingMedicineNuclear medicineRadiation therapyRadiologyPhysicsTelecommunications

Abstract

fetched live from OpenAlex

BACKGROUND: Electromagnetic tracking (EMT) systems have proven to be a valuable source of information regarding the location and geometry of applicators in patients undergoing brachytherapy (BT). As an important element of an enhanced and individualized pre-treatment verification, EMT can play a pivotal role in detecting treatment errors and uncertainties to increase patient safety. PURPOSE: The purpose of this study is two-fold: to design, develop and test a dedicated measurement protocol for the use of EMT-enabled afterloaders in BT and to collect and compare the data acquired from three different radiation oncology centers in different clinical environments. METHODS: A novel quality assurance (QA) phantom composed of a scaffold with supports to fix the field generator, different BT applicators, and reference sensors (sensor verification tools) was used to assess the precision (jitter error) and accuracy (relative distance errors and target registration error) of the EMT sensor integrated into an afterloader prototype. Measurements were repeated in different environments where EMT measurements are likely to be performed, namely an electromagnetically clean laboratory, a BT suite, an operating room, and, if available, a CT suite and an MRI suite dedicated to BT. RESULTS: The mean positional jitter was consistently under 0.1 mm across all measurement points, with a slight trend of increased jitter at greater distances from the field generator. The mean variability of sensor positioning in the tested tandem and ring gynecological applicator was also below 0.1 mm. The tracking accuracy close to the center of the measurement volume was higher than at its edges. The relative distance error at the center was 0.2-0.3 mm with maximum values reaching 1.2-1.8 mm, but up to 5.5 mm for measurement points close to the edges. In general, similar accuracy results were obtained in the clinical environments and in all investigated institutions (median distance error 0.1-0.4 mm, maximum error 1.0-2.0 mm), however, errors were found to be larger in the CT suite (median distance error up to 1.0 mm, maximum error up to 3.6 mm). CONCLUSION: The presented quality assessment protocol for EMT systems in BT has demonstrated that EMT offers a high-accuracy determination of the applicator/implant geometry even in clinical environments. In addition to that, it has provided valuable insights into the performance of EMT-enabled afterloaders across different radiation oncology centers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.401
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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