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Record W1965248034 · doi:10.1177/1533034614567277

Investigation of Dose Falloff for Intact Brain Metastases and Surgical Cavities Using Hypofractionated Volumetric Modulated Arc Radiotherapy

2015· article· en· W1965248034 on OpenAlexaff
Mark Ruschin, Young Lee, D Beachey, Collins Yeboah, Matt Wronski, Steven Babic, F. Lochray, A. Nico, Luluel Khan, Hany Soliman, Arjun Sahgal

Bibliographic record

VenueTechnology in Cancer Research & Treatment · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersSun Nuclear CorporationAshland
KeywordsNuclear medicineMedicineRadiation therapyBrain tissueRadiation treatment planningRadiosurgeryRadiologyBiomedical engineering

Abstract

fetched live from OpenAlex

INTRODUCTION: Intact brain metastases tend to be small and spherical compared to postsurgery brain cavities, which tend to be large and irregular shaped and, as a result, a challenge with respect to treatment planning. The purpose of the present study is to develop guidelines for normal brain tissue dose and to investigate whether there is a dependence on target type for patients treated with hypofractionated volumetric modulated arc radiotherapy (HF-VMAT). METHODS: Treatment plans from a total of 100 patients and 136 targets (55 cavity and 81 intact) were retrospectively reviewed. All targets were treated with HF-VMAT with total doses ranging between 20 and 30 gray (Gy) in 5 fractions. All plans met institutional objectives for organ-at-risk constraints and were clinically delivered. Dose falloff was quantified using gradient index (GI) and distance between the 100% and 50% isodose lines (R50). Additionally, the dose to normal brain tissue (brain contour excluding all gross tumor or clinical target volumes) was assessed using volume receiving specific doses (Vx) where x ranged from 5 to 30 Gy. Best-fit curves using power law relationships of the form y = ax(b) were generated for GI, R50, and Vx (normal brain tissue) versus target volume. RESULTS: There was a statistically significant difference in planning target volume (PTV) for cavities versus intact metastases with mean volumes of 37.8 cm(3) and 9.5 cm(3), respectively (P < .0001). The GI and R50 were statistically different: 3.4 and 9.8 mm for cavities versus 4.6 and 8.3 mm for intact metastases (P < .0001). The R50 increased with PTV with power law coefficients (a, b) = (6.3, 0.12) and (5.9, 0.15) for cavities and intact, respectively. GI decreased with PTV with coefficients (a, b) = (5.9, -0.18) and (5.7, -0.14) for cavities and intact, respectively. The normal brain tissue Vx also exhibited power law relationships with PTV for x = 20 to 28.8 Gy. In conclusion, target volume is the main predictor of dose falloff. The results of the present study can be used for determining target volume-based thresholds for dose falloff and normal brain tissue dose-volume constraints.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.140
GPT teacher head0.442
Teacher spread0.302 · 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 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

Citations9
Published2015
Admission routes1
Has abstractyes

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