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Record W4392987765 · doi:10.1002/mp.17020

First measurements of radon‐220 diffusion in mice tumors, towards treatment planning in diffusing alpha‐emitters radiation therapy

2024· article· en· W4392987765 on OpenAlexaff
Guy Heger, Mirta Dumančić, Ishai Luz, Maayan Vatarescu, Noam Weizman, Brian W. Miller, Tomer Cooks, L. Arazi

Bibliographic record

VenueMedical Physics · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsMcGill University
FundersAlphaTau Medical
KeywordsAlpha (finance)DiffusionNuclear medicineRadonDartRadiation transportIn vivoDosimetryAlpha particleRadiationRadiation therapyRadiumChemistryRadiochemistryMaterials sciencePhysicsMedicineAtomic physicsMonte Carlo methodThermodynamicsBiologyMathematicsNuclear physicsComputer scienceStatisticsSurgery

Abstract

fetched live from OpenAlex

Abstract Background Diffusing alpha‐emitters radiation therapy (“Alpha‐DaRT”) is a new method for treating solid tumors with alpha particles, relying on the release of the short‐lived alpha‐emitting daughter atoms of radium‐224 from interstitial sources inserted into the tumor. Alpha‐DaRT tumor dosimetry is governed by the spread of radium's progeny around the source, as described by an approximate framework called the “diffusion‐leakage model”. The most important model parameters are the diffusion lengths of radon‐220 and lead‐212, and their estimation is therefore essential for treatment planning. Purpose Previous works have provided initial estimates for the dominant diffusion length, by measuring the activity spread inside mice‐borne tumors several days after the insertion of an Alpha‐DaRT source. The measurements, taken when lead‐212 was in secular equilibrium with radium‐224, were interpreted as representing the lead‐212 diffusion length. The aim of this work is to provide first experimental estimates for the diffusion length of radon‐220, using a new methodology. Methods The diffusion length of radon‐220 was estimated from autoradiography measurements of histological sections taken from 24 mice‐borne subcutaneous tumors of five different types. Unlike previous studies, the source dwell time inside the tumor was limited to 30 min, to prevent the buildup of lead‐212. To investigate the contribution of potential non‐diffusive processes, experiments were done in two sets: fourteen in vivo tumors, where during the treatment the tumors were still carried by the mice with active blood supply, and 10 ex‐vivo tumors, where the tumors were excised before source insertion and kept in a medium at with the source inside. Results The measured diffusion lengths of radon‐220, extracted by fitting the recorded activity pattern up to 1.5 mm from the source, lie in the range , with no significant difference between the average values measured in in‐vivo and ex‐vivo tumors: versus . However, in‐vivo tumors display an enhanced spread of activity 2–3 mm away from the source. This effect is not explained by the current model and is much less pronounced in ex‐vivo tumors. Conclusions The average measured radon‐220 diffusion lengths in both in‐vivo and ex‐vivo tumors are consistent with published data on the diffusion length of radon in water and lie close to the upper limit of the previously estimated range of . The observation that close to the source there is no apparent difference between in‐vivo and ex‐vivo tumors, and the good agreement with the theoretical model in this region suggest that the spread of radon‐220 is predominantly diffusive in this region. The departure from the model prediction in in‐vivo tumors at large radial distances may hint at potential vascular contribution, which will be the subject of future works.

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: Empirical · Consensus signal: Empirical
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 head0.068
GPT teacher head0.356
Teacher spread0.288 · 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
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

Citations7
Published2024
Admission routes1
Has abstractyes

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