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Record W2004361765 · doi:10.1118/1.3693049

<i>In vivo</i> dosimetry for gynaecological brachytherapy using a novel position sensitive radiation detector: Feasibility study

2012· article· en· W2004361765 on OpenAlexaffabout
Brigitte Reniers, Guillaume Landry, R. Eichner, A Hallil, Frank Verhaegen

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsDosimetryDosimeterImaging phantomDetectorBrachytherapyDose profileMedical physicsNuclear medicinePosition (finance)Medical imagingPhysicsMaterials scienceRadiation therapyOpticsMedicineRadiology

Abstract

fetched live from OpenAlex

PURPOSE: In gynecological radiotherapy with high dose rate (HDR)(192)Ir brachytherapy, the treatment complexity has increased due to improved optimization techniques and dose constraints. As a consequence, it has become more important to verify the dose delivery to the target and also to the organs at risk (e.g., the bladder). In vivo dosimetry, where dosimeters are placed in or on the patient, is one way of verifying the dose but until recently this was hampered by motion of the radiation detectors with respect to the source. The authors present a novel dosimetry method using a position sensitive radiation detector. METHODS: The prototype RADPOS system (Best Medical Canada) consists of a metal oxide field effect transistor (MOSFET) dosimeter coupled to a position-sensor, which deduces its 3D position in a magnetic field. To assess the feasibility of in vivo dosimetry based on the RADPOS system, different characteristics of the detector need to be investigated. Using a PMMA phantom, the positioning accuracy of the RADPOS system was quantified by comparing position readouts with the known position of the detector along the x and y-axes. RADPOS dose measurements were performed at various distances from a Nucletron(192)Ir source in a PMMA phantom to evaluate the energy dependence of the MOSFET. A sensitivity analysis was performed by calculating the dose after varying (1) the position of the RADPOS detector to simulate organ motion and (2) the position of the first dwell position to simulate errors in delivery. The authors also performed an uncertainty analysis to determine the action level (AL) that should be used during in vivo dosimetry. RESULTS: Positioning accuracy is found to be within 1 mm in the 1-10 cm range from the origin along the x-axis (away from the transmitter), meeting the requirements for in vivo dosimetry. Similar results are obtained for the other axes. The ALs are chosen to take into account the total uncertainty on the measurements. As a consequence for in vivo dosimetry, it is determined that the RADPOS sensor, if placed, for example, in the bladder Foley balloon, would detect a 2 mm motion of the bladder, at a 5% chance of a false positive, with an AL limit of 9% of the dose delivered. The authors found that source position errors, caused by, e.g., a wrong first dwell position, are more difficult to detect; indeed, with our single RADPOS detector, positioned in the bladder, dwell position errors below 5 mm and resulting in a dose error within 10%, could be detected in the tandem but not in the colpostats. A possible solution to improve error detection is to use multiple MOSFETs to obtain multiple dose values. CONCLUSIONS: In this study, the authors proposed a dosimetry procedure, based on the novel RADPOS system, to accurately determine the position of the radiation dosimeter with respect to the applicator. The authors found that it is possible to monitor the delivered dose in a point and compare it to the predetermined dose. This allows in principle the detection of problems such as bladder motion/filling or source mispositioning. Further clinical investigation is warranted.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.722
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.343
Teacher spread0.316 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations27
Published2012
Admission routes2
Has abstractyes

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