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Record W2065974229 · doi:10.1118/1.4888333

SU‐E‐T‐04: 3D Printed Patient‐Specific Surface Mould Applicators for Brachytherapy Treatment of Superficial Lesions

2014· article· en· W2065974229 on OpenAlexaff
Iain Cumming, Chandra P. Joshi, András Lassó, Adam Rankin, Conrad Falkson, L J Schreiner, Gábor Fichtinger

Bibliographic record

VenueMedical Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsCancer Care South EastKingston General HospitalQueen's University
Fundersnot available
KeywordsImaging phantomStereolithography3d printedBrachytherapy3d printerBiomedical engineeringMaterials science3D printingCatheterFiducial markerComputer scienceEngineering drawingMechanical engineeringNuclear medicineSurgeryMedicineComputer visionEngineeringComposite materialRadiation therapy

Abstract

fetched live from OpenAlex

Purpose: Evaluate the feasibility of constructing 3D‐printed patient‐specific surface mould applicators for HDR brachytherapy treatment of superficial lesions. Methods: We propose using computer‐aided design software to create 3D printed surface mould applicators for brachytherapy. A mould generation module was developed in the open‐source 3D Slicer ( www.slicer.org ) medical image analysis platform. The system extracts the skin surface from CT images, and generates smooth catheter paths over the region of interest based on user‐defined start and end points at a specified stand‐off distance from the skin surface. The catheter paths are radially extended to create catheter channels that are sufficiently wide to ensure smooth insertion of catheters for a safe source travel. An outer mould surface is generated to encompass the channels. The mould is also equipped with fiducial markers to ensure its reproducible placement. A surface mould applicator with eight parallel catheter channels of 4mm diameters was fabricated for the nose region of a head phantom; flexible plastic catheters of 2mm diameter were threaded through these channels maintaining 10mm catheter separations and a 5mm stand‐off distance from the skin surface. The apparatus yielded 3mm thickness of mould material between channels and the skin. The mould design was exported as a stereolithography file to a Dimension SST1200es 3D printer and printed using ABS Plus plastic material. Results: The applicator closely matched its design and was found to be sufficiently rigid without deformation during repeated application on the head phantom. Catheters were easily threaded into channels carved along catheter paths. Further tests are required to evaluate feasibility of channel diameters smaller than 4mm. Conclusion: Construction of 3D‐printed mould applicators show promise for use in patient specific brachytherapy of superficial lesions. Further evaluation of 3D printing techniques and materials is required for constructing sufficiently thin, rigid and durable surface moulds suitable for clinical deployment.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.001

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 head0.015
GPT teacher head0.290
Teacher spread0.275 · 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".

Quick stats

Citations6
Published2014
Admission routes1
Has abstractyes

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