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Record W4413122156 · doi:10.1101/2025.08.02.25332864

Precise dose verification in proton therapy using Positron Emission Tomography

2025· preprint· en· W4413122156 on OpenAlexaff
Marcin Balcerzyk, Marta Freire, Rubén Fernández de la Rosa, Miguel A. Pozo, Rhodri Smith, G. Sanchez-Merino, Antonio J. González

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPositron emission tomographyProton therapyImaging phantomNuclear medicineLyso-PositronProtonIn vivoPositron emissionRadiation therapyMaterials sciencePhysicsMedicineDetectorNuclear physicsRadiologyOptics

Abstract

fetched live from OpenAlex

Abstract Background Proton and ion therapy have gained significant importance in radiation therapy cancer treatment due to their favorable dose distribution and tissue-sparing properties. In conventional gamma radiation therapy some methods of in vivo dose verification are possible with current medical devices. Proton and ion therapy dose verification is limited, mainly using PET for particle range. Prompt gamma methods offer low spatial resolution. This study presents initial results for in-vivo dose verification with PET imaging of F-18 during proton therapy. Although the activity concentration of F-18 generated by typical clinical doses (several Gy) is low, PET imaging performed approximately one hour post-irradiation yields sufficient image quality to derive dose-volume histograms (DVH), enabling spatial dose verification. Purpose To verify the applied dose in proton therapy in vivo using Positron Emission Tomography with millimetric precision. Materials and Methods We simulated proton treatment in a brain phantom using Gate and RayStation platforms to assess the production of several positron emitting isotopes. We focused on the production of fluorine-18 (F-18), given its low positron energy, which enables accurate reproduction of the dose distribution. To evaluate the detectability of the anticipated low activity concentrations (on the order of a few Bq/mL) following a 3 Gy proton irradiation, we tested three PET systems: two preclinical scanners based on LYSO detectors and one clinical scanner based on BGO crystals. Finally, we have analyzed the dose-volume histograms for simulated and measured dose and activity distributions and compared them with the planned ones. Results F-18 PET imaging in proton therapy correlates with delivered dose within 5% error and matches the planned dose fall-off edge within 1 mm, enabling accurate and precise in vivo dose verification. Conclusion The dose verification in proton therapy using F-18 Positron Emission Tomography allows higher precision of dose than other positron emitters like C-11, N-12 or O-15. 1 Key Results Key Results: In proton therapy, in-vivo F-18 production correlates with the deposited dose in the patient with a 5% margin of error. The leading-edge position of the F-18 activity distribution agrees with the planned dose fall off within 1 mm. Furthermore, the feasibility of detecting low activity concentrations typical of proton therapy (Bq/ml range) has been demonstrated using both preclinical and clinical PET scanners. 2 Required Summary Statement In proton therapy the delivered dose to the patient can be measured in vivo using Positron Emission Tomography by imaging the production of F-18 isotope, achieving millimetric spatial accuracy.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 head0.039
GPT teacher head0.341
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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