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Record W4404520610 · doi:10.1002/mp.17525

RapidBrachyIVBT: A dosimetry software for patient‐specific intravascular brachytherapy dose calculations on optical coherence tomography images

2024· article· en· W4404520610 on OpenAlexafffund
Maryam Rahbaran, Jonathan Kalinowski, Joseph M. DeCunha, Kevin Croce, Brian A. Bergmark, James Man Git Tsui, Phillip M. Devlin, Shirin A. Enger

Bibliographic record

VenueMedical Physics · 2024
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsMcGill University Health CentreMcGill UniversityJewish General Hospital
FundersCanada Research Chairs
KeywordsDosimetryBrachytherapyMedical physicsOptical coherence tomographyNuclear medicineMedical imagingRadiologyMedicineRadiation therapy

Abstract

fetched live from OpenAlex

Abstract Background Coronary artery disease is the most common form of cardiovascular disease. It is caused by excess plaque along the arterial wall, blocking blood flow to the heart (stenosis). A percutaneous coronary intervention widens the arterial wall with the inflation of a balloon inside the lesion area and leaves behind a metal stent to prevent re‐narrowing of the artery (restenosis). However, in‐stent restenosis may occur due to damage to the arterial wall tissue, triggering neointimal hyperplasia, producing fibrotic and calcified plaques and narrowing the artery again. Drug‐eluting stents, which slowly release medication to inhibit neointimal hyperplasia, are used to prevent in‐stent restenosis but fail up to 20% of cases. Coronary intravascular brachytherapy (IVBT), which uses ‐emitting radionuclides to prevent in‐stent restenosis, is used in these failed cases to prevent in‐stent restenosis. However, current clinical dosimetry for IVBT is water‐based, and heterogeneities such as the guidewire of the IVBT device, fibrotic and calcified plaques and stents are not considered. Purpose This study aimed to develop a Monte Carlo‐based dose calculation software, accounting for patient‐specific geometry from Optical Coherence Tomography (OCT) images. Methods RapidBrachyIVBT, a Monte Carlo dose calculation software based on the Geant4 toolkit v. 10.02.p02, was developed and integrated into RapidBrachyMCTPS, a treatment planning system for brachytherapy applications. The only commercially available IVBT delivery system, the Novoste Beta‐Cath 3.5F, with a source train, was modeled with 30, 40, and 60 mm source train lengths. The software was validated with published TG‐149 parameters compared to Monte Carlo simulations in water. The dose calculation engine was tested with OCT images from a patient undergoing coronary IVBT for recurrent in‐stent restenosis at Brigham and Women's Hospital in Boston, Massachusetts. Considering the heterogeneities, the images were segmented and used to calculate the absorbed dose to water and the absorbed dose to medium. The prescribed dose was normalized to 23 Gy at 2.0 mm from the source center, which is the target volume in IVBT. Results The dose rate values in water obtained using RapidBrachyIVBT aligned with TG‐149 consensus values, showing agreement within a range of 0.03% to 1.7%. Considering the heterogeneities present in the patient's OCT images, the absorbed dose in the entire artery segment was up to 77.5% lower, while within the target volume, it was up to 56.6% lower, compared to the dose calculated in a homogeneous water phantom. Conclusion RapidBrachyIVBT, a Monte Carlo dose calculation software for IVBT, was developed and successfully integrated into RapidBrachyMCTPS, a treatment planning system for brachytherapy applications, where accurate attenuation of the absorbed dose by heterogeneities is considered.

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)0.001
metaresearch head score (Gemma)0.003
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: Software · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.004

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 head0.017
GPT teacher head0.297
Teacher spread0.281 · 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
GenreSoftware

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

Citations5
Published2024
Admission routes2
Has abstractyes

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