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Record W2791992138 · doi:10.1002/acm2.12251

To frame or not to frame? Cone‐beam CT‐based analysis of head immobilization devices specific to linac‐based stereotactic radiosurgery and radiotherapy

2018· article· en· W2791992138 on OpenAlexaff
Steven Babic, Young Lee, Mark Ruschin, F. Lochray, Alex W. Lightstone, Eshetu G. Atenafu, Nic Phan, Todd G. Mainprize, May Tsao, Hany Soliman, Arjun Sahgal

Bibliographic record

VenueJournal of Applied Clinical Medical Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsHealth Sciences CentrePrincess Margaret Cancer CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsRadiosurgeryMedicineCone beam computed tomographyNuclear medicineLinear particle acceleratorImage-guided radiation therapyRadiation therapyReproducibilityStereotactic radiotherapyRadiologyMedical physicsBeam (structure)Computed tomographyOpticsPhysicsMathematics

Abstract

fetched live from OpenAlex

PURPOSE: Noninvasive frameless systems are increasingly being utilized for head immobilization in stereotactic radiosurgery (SRS). Knowing the head positioning reproducibility of frameless systems and their respective ability to limit intrafractional head motion is important in order to safely perform SRS. The purpose of this study was to evaluate and compare the intrafractional head motion of an invasive frame and a series of frameless systems for single fraction SRS and fractionated/hypofractionated stereotactic radiotherapy (FSRT/HF-SRT). METHODS: The noninvasive PinPoint system was used on 15 HF-SRT and 21 SRS patients. Intrafractional motion for these patients was compared to 15 SRS patients immobilized with Cosman-Roberts-Wells (CRW) frame, and a FSRT population that respectively included 23, 32, and 15 patients immobilized using Gill-Thomas-Cosman (GTC) frame, Uniframe, and Orfit. All HF-SRT and FSRT patients were treated using intensity-modulated radiation therapy on a linear accelerator equipped with cone-beam CT (CBCT) and a robotic couch. SRS patients were treated using gantry-mounted stereotactic cones. The CBCT image-guidance protocol included initial setup, pretreatment and post-treatment verification images. The residual error determined from the post-treatment CBCT was used as a surrogate for intrafractional head motion during treatment. RESULTS: The mean intrafractional motion over all fractions with PinPoint was 0.62 ± 0.33 mm and 0.45 ± 0.33 mm, respectively, for the HF-SRT and SRS cohort of patients (P-value = 0.266). For CRW, GTC, Orfit, and Uniframe, the mean intrafractional motions were 0.30 ± 0.21 mm, 0.54 ± 0.76 mm, 0.73 ± 0.49 mm, and 0.76 ± 0.51 mm, respectively. For CRW, PinPoint, GTC, Orfit, and Uniframe, intrafractional motion exceeded 1.5 mm in 0%, 0%, 5%, 6%, and 8% of all fractions treated, respectively. CONCLUSIONS: The noninvasive PinPoint system and the invasive CRW frame stringently limit cranial intrafractional motion, while the latter provides superior immobilization. Based on the results of this study, our clinical practice for malignant tumors has evolved to apply an invasive CRW frame only for metastases in eloquent locations to minimize normal tissue exposure.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

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)0.0010.000

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 head0.041
GPT teacher head0.395
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 designObservational
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

Citations62
Published2018
Admission routes1
Has abstractyes

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