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Record W1877166016 · doi:10.1118/1.4924950

SU‐E‐T‐587: Optimal Volumetric Modulated Arc Radiotherapy Treatment Planning Technique for Multiple Brain Metastases with Increasing Number of Arcs

2015· article· en· W1877166016 on OpenAlexaff
V Keeling, Shakhawoat Hossain, K Hildebrand, Salahuddin Ahmad, Larson Da, Arjun Sahgal, Lijun Ma

Bibliographic record

VenueMedical Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConventional PCINuclear medicineBrain tissueRadiation treatment planningMedicineRadiation therapyBiomedical engineeringRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Purpose: To show improvements in dose conformity and normal brain tissue sparing using an optimal planning technique (OPT) against clinically acceptable planning technique (CAP) in the treatment of multiple brain metastases. Methods: A standardized international benchmark case with12 intracranial tumors was planned using two different VMAT optimization methods. Plans were split into four groups with 3, 6, 9, and 12 targets each planned with 3, 5, and 7 arcs using Eclipse TPS. The beam geometries were 1 full coplanar and half non‐coplanar arcs. A prescription dose of 20Gy was used for all targets. The following optimization criteria was used (OPT vs. CAP): (No upper limit vs.108% upper limit for target volume), (priority 140–150 vs. 75–85 for normal‐brain‐tissue), and (selection of automatic sparing Normal‐Tissue‐Objective (NTO) vs. Manual NTO). Both had priority 50 to critical structures such as brainstem and optic‐chiasm, and both had an NTO priority 150. Normal‐brain‐tissue doses along with Paddick Conformity Index (PCI) were evaluated. Results: In all cases PCI was higher for OPT plans. The average PCI (OPT,CAP) for all targets was (0.81,0.64), (0.81,0.63), (0.79,0.57), and (0.72,0.55) for 3, 6, 9, and 12 target plans respectively. The percent decrease in normal brain tissue volume (OPT/CAP*100) achieved by OPT plans was (reported as follows: V4, V8, V12, V16, V20) (184, 343, 350, 294, 371%), (192, 417, 380, 299, 360%), and (235, 390, 299, 281, 502%) for the 3, 5, 7 arc 12 target plans, respectively. The maximum brainstem dose decreased for the OPT plan by 4.93, 4.89, and 5.30 Gy for 3, 5, 7 arc 12 target plans, respectively. Conclusion: Substantial increases in PCI, critical structure sparing, and decreases in normal brain tissue dose were achieved by eliminating upper limits from optimization, using automatic sparing of normal tissue function with high priority, and a high priority to normal brain tissue.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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)0.0020.000

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 head0.025
GPT teacher head0.325
Teacher spread0.301 · 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 designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

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