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Record W4389162835 · doi:10.1088/1361-6560/ad111a

Non-coplanar lung SABR treatments delivered with a gantry-mounted x-ray tube

2023· article· en· W4389162835 on OpenAlexafffund
J. William O’Connell, M Weil, Magdalena Bazalova‐Carter

Bibliographic record

VenuePhysics in Medicine and Biology · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSABR volatility modelTube (container)X-rayX-ray tubeNuclear medicineLungPhysicsMaterials scienceMedicineOpticsMathematicsInternal medicine

Abstract

fetched live from OpenAlex

Abstract Objective. To create two non-coplanar, stereotactic ablative radiotherapy (SABR) lung patient treatment plans compliant with the radiation therapy oncology group (RTOG) 0813 dosimetric criteria using a simple, isocentric, therapy with kilovoltage arcs (SITKA) system designed to provide low cost external radiotherapy treatments for low- and middle-income countries (LMICs). Approach. A treatment machine design has been proposed featuring a 320 kVp x-ray tube mounted on a gantry. A deep learning cone-beam CT (CBCT) to synthetic CT (sCT) method was employed to remove the additional cost of planning CTs. A novel inverse treatment planning approach using GPU backprojection was used to create a highly non-coplanar treatment plan with circular beam shapes generated by an iris collimator. Treatments were planned and simulated using the TOPAS Monte Carlo (MC) code for two lung patients. Dose distributions were compared to 6 MV volumetric modulated arc therapy (VMAT) planned in Eclipse on the same cases for a Truebeam linac as well as obeying the RTOG 0813 protocols for lung SABR treatments with a prescribed dose of 50 Gy. Main results. The low-cost SITKA treatments were compliant with all RTOG 0813 dosimetric criteria. SITKA treatments showed, on average, a 6.7 and 4.9 Gy reduction of the maximum dose in soft tissue organs at risk (OARs) as compared to VMAT, for the two patients respectively. This was accompanied by a small increase in the mean dose of 0.17 and 0.30 Gy in soft tissue OARs. Significance. The proposed SITKA system offers a maximally low-cost, effective alternative to conventional radiotherapy systems for lung cancer patients, particularly in low-income countries. The system’s non-coplanar, isocentric approach, coupled with the deep learning CBCT to sCT and GPU backprojection-based inverse treatment planning, offers lower maximum doses in OARs and comparable conformity to VMAT plans at a fraction of the cost of conventional radiotherapy.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.049
GPT teacher head0.372
Teacher spread0.323 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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