Boron neutron capture therapy in the new age of accelerator-based neutron production and preliminary progress in Canada
Bibliographic record
Abstract
Each year more than 3000 Canadians are diagnosed with brain cancers like glioblastoma multiforme or recurrent head and neck cancers, which are difficult to treat with conventional radiotherapy techniques. One of the most clinically promising treatments for these cancers is boron neutron capture therapy (BNCT). This procedure involves selectively introducing a boron delivery agent into tumor cells and irradiating them with a neutron beam, which kills the cancer cells due to the high-linear energy transfer radiation produced by the 10B(n,α)7Li capture reaction. The theory of BNCT has been around for a long time since 1936, but has historically been limited by poor boron delivery agents and non-optimal neutron source facilities. Although significant improvements have been made in both of these domains, it is mainly the advancements of accelerator-based neutron sources that have led to the expansion of over 20 new BNCT facilities worldwide in the past decade. Additionally in this work, particle and heavy ion transport code system simulations, in collaboration with the University of Tsukuba, were performed to examine the effectiveness of the Ibaraki BNCT beam shaping assembly to moderate a neutron beam suitable for BNCT at the proposed prototype Canadian compact accelerator-based neutron source (CANS) site, which uses a similar but slightly higher energy 10 MeV proton accelerator with a 1 mA average current. The advancements of CANSs in recent decades have enabled significant improvements in BNCT technologies, allowing it to become a more viable clinical treatment option.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".