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Record W2508357792 · doi:10.1016/s0167-8140(16)33598-8

199: Cone Beam Computer Tomography (CBCT) Verification of Stereotactic Body Radiotherapy (SBRT): A Prospective Analysis on the Concordance Between Radiation Therapists and Radiation Oncologists

2016· article· en· W2508357792 on OpenAlexaff
Rosanna Yeung, James Sidney, Robynn Ferris, Jenny Soo, Mitchell Liu

Bibliographic record

VenueRadiotherapy and Oncology · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineConcordanceCone beam computed tomographyMedical physicsNuclear medicineRadiologyCone beam ctRadiation therapyRadiation doseRadiation oncologyComputed tomographyInternal medicine

Abstract

fetched live from OpenAlex

Purpose: Image-guidance to ensure correct patient set-up prior to treatment delivery is of utmost importance in SBRT.Many studies have demonstrated high levels of concordance between radiation therapists (RTs) and radiation oncologists (ROs) on daily verification based on electronic portal imaging and simulator films.There is limited data, however, on the concordance between RTs and ROs using CBCT pre-treatment images prior to SBRT.The primary objective of this study is to evaluate the level of concordance between RTs and ROs on CBCT pre-treatment verification images for SBRT.Methods and Materials: Twenty-one consecutively treated SBRT patients at a provincial institution were included in the study.CBCT was performed by both RO and RTs prior to delivery of each fraction for abdominal and bone SBRT treatments, and prior to first fraction for lung SBRT.RTs performed subsequent daily matches for lung SBRT.Initial CBCT match was performed by RTs as per centre guidelines or by previously specified instructions.ROs performed second match prior to SBRT delivery.Directional differences between RT and RO match were recorded and analyzed.Intraclass correlation coefficients were calculated to determine variability between RT and RO match, with values approaching 1 suggesting high concordance.Results: Thirty-six CBCT images were analyzed.Thirteen were lung, 20 abdominal (five liver, 15 pancreas) and three were bone SBRT cases.RO made adjustments to initial RT match in 12 (33%) of CBCT matches, with six matches having a direction shift of 0.1 to < 0.2 cm in any direction, and two matches with a directional shift of ≥ 0.2 cm.Mean maximum match difference was 0.12 cm (range: 0.02, 0.31 cm).Treatments requiring adjustment to the initial RT match did not differ by treatment site.Review of the two plans with match difference of ≥ 0.2 cm revealed no appreciable change in GTV coverage and with dose to organs at risk still within accepted constraints.Intraclass correlation coefficients for all CBCT matches were 0.998 (95% CI: 0.996-0.999),0.997 (95% CI: 0.995-0.999),0.996 (95% CI: 0.992-0.998) in the x, y, and z directions respectively.Intraclass correlation coefficients for CBCT matches that required adjustment by RO were similar at 0.995 (95%CI: 0.982-0.9980.982-0.998),0.995 (95% CI: 0.984-0.9990.984-0.999),0.989 (95% CI: 0.962-0.997) in the x, y, and z directions respectively.Conclusions: CBCT match for SBRT at our center shows high concordance between RTs and ROs.These findings support potential expansion of RT scope of practice at our institution to include independent CBCT matches for SBRT.

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)0.002
metaresearch head score (Gemma)0.011
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.010
GPT teacher head0.291
Teacher spread0.282 · 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".

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Citations0
Published2016
Admission routes1
Has abstractno

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