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Record W4284893856 · doi:10.1080/24725579.2022.2027051

Optimization of sliding windows IMRT treatment planning

2022· article· en· W4284893856 on OpenAlexafffund
Rafiq R. Habib, Jessie Yeung, Johnson Darko, Ernest Osei, Houra Mahmoudzadeh

Bibliographic record

VenueIISE Transactions on Healthcare Systems Engineering · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsGrand River HospitalUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCollimatorRadiation treatment planningLinear particle acceleratorProcess (computing)RadiationComputer scienceRadiation therapyIrradiationBeam (structure)SimulationOpticsPhysicsMedicineRadiology

Abstract

fetched live from OpenAlex

Intensity-modulated radiation therapy (IMRT) with sliding windows is a form of radiation therapy that delivers precise radiation dose to a tumor/target region. It uses a multi-leaf collimator (MLC) to move pairs of unidirectional tungsten leaves across a radiation emitting region to conform the shape of the radiation beam to the target regions. This is a dynamic treatment approach which aims to deliver adequate radiation dose to target regions while minimizing radiation delivery to healthy tissues. This paper proposes a linear optimization model for IMRT with sliding windows. This model directly incorporates a number of deliverability constraints to conform to physical limitations of the LINAC, including the required distance between leaves through the treatment process and restrictions on leaf interdigitation. We demonstrate the viability of this model using patient data and discuss the leaf motion proposed by our model. Such a model can be embedded in treatment planning systems to improve both the quality of the treatment and the efficiency of the treatment planning process.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.681

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 head0.017
GPT teacher head0.276
Teacher spread0.259 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2022
Admission routes2
Has abstractyes

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