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Record W4386530012 · doi:10.1002/mp.16709

Robust mixed electron‐photon radiation therapy planning for soft tissue sarcoma

2023· article· en· W4386530012 on OpenAlexafffund
Veng Jean Heng, Monica Serban, Marc‐André Renaud, Carolyn Freeman, Jan Seuntjens

Bibliographic record

VenueMedical Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsMcGill University Health CentrePrincess Margaret Cancer CentreUniversity of TorontoMcGill University
FundersFonds de recherche du Québec – Nature et technologiesCanadian Institutes of Health Research
KeywordsDosimetryNuclear medicineRadiation therapyLinear particle acceleratorCollimatorBolus (digestion)PhotonCollimated lightElectronSarcomaMaterials scienceMedicinePhysicsBeam (structure)RadiologyOpticsSurgeryPathologyLaserNuclear physics

Abstract

fetched live from OpenAlex

Abstract Background Mixed electron‐photon beam radiation therapy (MBRT) is an emerging technique in which external electron and photon beams are simultaneously optimized into a single treatment plan. MBRT exploits the steep dose falloff and high surface dose of electrons while maintaining target conformity by leveraging the sharp penumbra of photons. Purpose This study investigates the dosimetric benefits of MBRT for soft tissue sarcoma (STS) patients. Material and methods A retrospective cohort of 22 STS of the lower extremity treated with conventional photon‐based Volumetric Modulated Arc Therapy (VMAT) were replanned with MBRT. Both VMAT and MBRT treatments were planned on the Varian TrueBeam linac using the Millenium multi‐leaf collimator. No electron applicator, cutout or additional collimating devices were used for electron beams of MBRT plans. MBRT plans were optimized to use a combination of 6 MV photons and five electron energies (6, 9, 12, 16, 20 MeV) by a robust column generation algorithm. Electron beams in this study were planned at standard 100 cm source‐axis distance (SAD). The dose to the clinical target volume (CTV), bone, normal tissue strip and other organs‐at‐risk (OARs) were compared using a Wilcoxon signed‐rank test. Results As part of the original VMAT treatment, tissue‐equivalent bolus was required in 10 of the 22 patients. MBRT plans did not require bolus by virtue of the higher electron entrance dose. CTV coverage by the prescription dose was found to be clinically equivalent between plans of either modality: (MBRT) = 97.9 ± 0.2% versus (VMAT) = 98.1 ± 0.6% (p=0.34). Evaluating the absolute paired difference between doses to OARs in MBRT and VMAT plans, we observed lower to normal tissue in MBRT plans by 14.9 ± 3.2% (). Similarly, to bone was found to be decreased by 8.2 ± 4.0% () of the bone volume. Conclusion For STS with subcutaneous involvement, MBRT offers statistically significant sparing of OARs without sacrificing target coverage when compared to VMAT. MBRT plans are deliverable on conventional linacs without the use of electron applicators, shortened source‐to‐surface distance (SSD) or bolus. This study shows that MBRT is a logistically feasible technique with clear dosimetric benefits.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 head0.024
GPT teacher head0.317
Teacher spread0.293 · 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 designSimulation or modeling
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

Citations4
Published2023
Admission routes2
Has abstractyes

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