Strategies for Preclinical Studies Evaluating the Biological Effects of an Accelerator-Based Boron Neutron Capture Therapy System
Bibliographic record
Abstract
This review discusses the strategies of preclinical studies intended for accelerator-based (AB)-boron neutron capture therapy (BNCT) clinical trials, which were presented at the National Cancer Institute (NCI) Workshop on Neutron Capture Therapy held from April 20 to 22, 2022. Clinical studies of BNCT have been conducted worldwide using reactor neutron sources, with most targeting malignant brain tumors, melanoma, or head and neck cancer. Recently, small accelerator-based neutron sources that can be installed in hospitals have been developed. AB-BNCT clinical trials for recurrent malignant glioma, head and neck cancers, high-grade meningioma, melanoma, and angiosarcoma have all been conducted in Japan. The necessary methods, equipment, and facilities for preclinical studies to evaluate the biological effects of AB-BNCT systems in terms of safety and efficacy are described, with reference to two examples from Japan. The first is the National Cancer Center, which is equipped with a vertical downward neutron beam, and the other is the University of Tsukuba, which has a horizontal neutron beam. The preclinical studies discussed include cell-based assays to evaluate cytotoxicity and genotoxicity, in vivo cytotoxicity and efficacy of BNCT, and radioactivation measurements.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".