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Record W2062963565 · doi:10.1118/1.4736309

TH‐C‐BRB‐06: Tools and Methods for On‐Line Adaptive Radiation Therapy

2012· article· en· W2062963565 on OpenAlexaffabout
Joe H. Chang, Robert K. Heaton, Yuan Horne Cho, David A. Jaffray, Md Shafiqul Islam

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDosimetryImage-guided radiation therapyComputer scienceRadiation treatment planningField (mathematics)Nuclear medicineMedical physicsMedical imagingArtificial intelligenceMathematicsRadiation therapyPhysicsMedicineSurgery

Abstract

fetched live from OpenAlex

Purpose: Essential tools required for daily field modifications for compensating small target variations have been investigated. A deformable registration method was used to generate deformation vector field (DVF), which is subsequently used to modify treatment fields. An on‐line beam monitor was evaluated for validating the adapted fields. Methods: An adaptive treatment process consists of the following steps: (1) Determine the DVF using the irradiated volumes (IV) of the reference and daily image sets. The IV is constructed by back‐projections of respective complete irradiation area outlines of all beams. (2) The deformed contours (CD) were calculated using DVF and the reference contours. (3) Determine MLC leaf adjustments using CD and DVF to form the adapted fields. This approach was investigated using two CT image sets, one acquired during the treatment course, and corresponding IMRT plan data for patients undergoing prostate treatment. A planning study was performed to compare the dose distributions between optimized IMRT and the adapted plans. The adapted field delivery was validated by an online beam monitoring system, termed IQM; which consists of a spatially sensitive area ion chamber mounted below the MLC to provide a fluence‐area‐product signal and a calculation module to predict the signal. Results: Approximately 10 minutes were required to generate the DVF between the reference IV and a daily IV using readily available computer hardware. The dose distribution using adapted fields compare well with the optimized plans in terms of DVH and conformity indices. The average agreement between the IQM predicted signals of the adapted fields with those of measured were within 2%. Conclusions: The method of generating the DVF was promising, in terms of speed and effectiveness for small variations of target. The IQM system was able to validate the delivery of appropriate adapted fields, fulfilling the requirement for pre‐treatment quality control. Ontario Consortium for Adaptive Interventions in Radiation Oncology (OCAIRO)

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)0.003
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.011

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 head0.044
GPT teacher head0.398
Teacher spread0.354 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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