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Record W2883592852 · doi:10.1002/mp.13102

Evaluation of the 4D <scp>RADPOS</scp> dosimetry system for dose and position quality assurance of CyberKnife

2018· article· en· W2883592852 on OpenAlexaff
R Marants, Eric Vandervoort, Joanna Cygler

Bibliographic record

VenueMedical Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsOttawa HospitalCarleton University
Fundersnot available
KeywordsCyberknifeImaging phantomQuality assuranceNuclear medicineMonte Carlo methodDosimetryRadiosurgeryPosition (finance)Radiation treatment planningComputer sciencePhysicsMedicineRadiation therapyBiomedical engineeringRadiologyMathematics

Abstract

fetched live from OpenAlex

PURPOSE: The Synchrony respiratory motion tracking of the CyberKnife system purports to provide real-time tumor motion compensation during robotic radiosurgery. Such a complex delivery system requires thorough quality assurance. In this work, RADPOS applicability as a dose and position quality assurance tool for CyberKnife treatments is assessed quantitatively for different phantom types and breathing motions, which increase in complexity to more closely resemble clinical situations. METHODS: Two radiotherapy treatment experiments were performed where dose and position were measured with the RADPOS probe housed within a Solid Water phantom. For the first experiment, a Solid Water breast phantom was irradiated using isocentric beam delivery while stationary or moving sinusoidally in the anterior/posterior direction. For the second experiment, a phantom consisting of a Solid Water tumor in lung equivalent material was irradiated using isocentric and non-isocentric beam delivery while either stationary or moving. The phantom movement was either sinusoidal or based on a real patient's breathing waveform. For each experiment, RADPOS dose measurements were compared to EBT3 GafChromic film dose measurements and the CyberKnife treatment planning system's (TPS) Monte Carlo and ray-tracing dose calculation algorithms. RADPOS position measurements were compared to measurements made by the CyberKnife system and to the predicted breathing motion models used by the Synchrony respiratory motion compensation. RESULTS: For the static and dynamic (i.e., sinusoidal motion) cases of the breast experiment, RADPOS, film and the TPS agreed at the 2.0% level within 1.1 σ of estimated combined uncertainties. RADPOS position measurements were in good agreement with LED and fiducial position measurements, where the average standard deviation (SD) of the differences between any two of the three position datasets was ≤0.5 mm for all directions. For the 10 mm peak to peak amplitude sinusoidal motion of the breast experiment, the average Synchrony correlation errors were ≤0.2 mm, indicative of an accurate predictive model. For all the cases of the lung experiment, RADPOS and film measurements agreed with each other at the 2.0% level within 1.5 σ of estimated experimental uncertainties provided that the measurements were corrected for imaging dose. The measured dose for RADPOS and film were 4.0% and 3.4% higher, respectively, than the TPS for the most complex dynamic cases (i.e., irregular motion) considered for the lung experiment. Assessment of the Synchrony correlation models by RADPOS showed that model accuracy declined as motion complexity increased; the SD of the differences between RADPOS and model position data measurements was ≤0.8 mm for sinusoidal motion but increased to ≤2.6 mm for irregular patient waveform motion. These results agreed with the Synchrony correlation errors reported by the CyberKnife system. CONCLUSIONS: RADPOS is an accurate and precise QA tool for dose and position measurements for CyberKnife deliveries with respiratory motion compensation.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.342
Teacher spread0.320 · 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 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".

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Citations10
Published2018
Admission routes1
Has abstractyes

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