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Validation of spine SBRT using a 3D printed Anthropomorphic phantom

2022· article· en· W4206993283 on OpenAlexaff
KH Dekker, K M Alexander, Fábio Ynoe de Moraes, T Olding

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsQueen's UniversityKingston General Hospital
Fundersnot available
KeywordsImaging phantomFiducial marker3d printedDosimeter3d printerNuclear medicineMaterials scienceDosimetryBiomedical engineeringComputer scienceMedicineRadiologyEngineering

Abstract

fetched live from OpenAlex

Abstract A segment of a spine was 3D-printed based on real patient anatomy, using metal-doped high density plastic to radiographically mimic bone. This spine was submerged in a water tank to create an anthropomorphic phantom. The spine print incorporated a slot for Gafchromic EBT3 film dosimeters and fiducials for alignment of measured and calculated dose distributions. Spine SBRT treatment plans were generated for both 6 MV and 10FFF energies based on oncologist-drawn contours transferred from real anatomy. Plans were delivered under image guidance using our clinical procedures, to evaluate the dosimetric accuracy of our planning system in high density inhomogeneities and the geometric accuracy of delivery. Results show that the Acuros XB algorithm (dose-to-water) agrees well with film measurements throughout the measured region, including within the bone substitute material. Alignment of the steep dose gradients in planned and measured doses was within 0.5 mm in the ANT-POST direction and within 0.9 mm in the SUP-INF direction, both within machine tolerances. Our results give us confidence in our ability to plan and accurately deliver spinal SBRT treatments.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.633
Threshold uncertainty score0.776

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)0.0010.000

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 head0.028
GPT teacher head0.308
Teacher spread0.279 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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