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Record W2019511418 · doi:10.1177/153303460900800506

Dosimetric Comparison of Intensity-Modulated Radiosurgery and Helical Tomotherapy for the Treatment of Multiple Intracranial Metastases

2009· article· en· W2019511418 on OpenAlexaff
Lauren VanderSpek, Glenn Bauman, Jia-Zhu Wang, Slav Yartsev, Cynthia Ménard, Young‐Bin Cho, Arno J. Mundt, Joshua D. Lawson, Kevin T. Murphy

Bibliographic record

VenueTechnology in Cancer Research & Treatment · 2009
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoWestern University
Fundersnot available
KeywordsTomotherapyMedicineNuclear medicineRadiosurgeryDosimetryRadiation therapyRadiology

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the dosimetry of single fraction, single-isocenter intensity-modulated radiosurgery (IMRS) plans for multiple intracranial metastases and to compare Helical Tomotherapy (HT). Ten treatment plans with 3-6 brain metastases treated with IMRS were re-planned with HT. The mean number of lesions was 5 and mean PTV 22 cm(3). The prescribed dose was 16-20 Gy. The mean V100% was similar for IMRS and HT, and the mean conformity index was 1.4, mean Paddick confirmity index was 0.7, and mean MDPD was 1.1 for both. The mean gradient index was similar for both. The mean 50% _isodose volume was 179.2 cm(3) for IMRS and 277.0 cm(3) for HT (p=0.01). The mean maximum doses to organs at risk were lower for IMRS except brainstem and right optic nerve. For brain, the integral dose was 5.1 and 6.8 Gy-kg (p<0.001) and mean dose 4.0 and 5.4 Gy (p<0.001) for IMRS and HT, respectively. The mean treatment times were 23 (IMRS) and 41 (HT) minutes. Conformity and homogeneity indices were equivalent and sparing of the organs at risk was clinically acceptable for both IMRS and HT. Though the gradient index was similar for IMRS and HT, the mean 50% isodose volume and integral dose to normal brain were lower for IMRS as was treatment time.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.120
GPT teacher head0.454
Teacher spread0.334 · 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 designOther design
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

Citations10
Published2009
Admission routes1
Has abstractyes

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