Design and optimization of imageable microspheres for locoregional cancer therapy
Bibliographic record
Abstract
Transarterial radioembolization (TARE) is an increasingly important technique for treating liver-based malignancies. Personalized treatment planning and dosimetry are not yet possible due to poor imageability of existing TARE agents. This study presents the design and development of a cohort of imageable glass microspheres that are compatible with readily available imaging equipment, including single-photon emission computed tomography (SPECT) and computed tomography (CT). A statistical modelling approach was used to investigate how the addition of holmium (Ho), a high atomic number and high k-edge element, to a Y2O3-Al2O3-SiO2 (YAS) glass matrix impacts material properties such as density, CT imageability, and glass transition temperature (Tg). The microspheres demonstrated excellent radiopacity, with Hounsfield Unit (HU) values ranging up to ~ 19,800 at 70 kVp, high thermal stability, exhibiting Tg values up to 895 °C, no cytotoxic potential, and negligible ion leaching pre- and post-irradiation to 2600 GBq/g Ho-166, supporting their safety and efficacy for locoregional therapies. Statistical modelling elucidated how the fraction of holmium oxide content within the glass matrix impacts density, CT imageability, and Tg. The ability to visualize the microspheres intra- and post-operatively via CT and SPECT imaging, combined with stable radionuclide incorporation and high achievable specific activity, marks a significant advancement in TARE, and represents an opportunity to expand applicability to cancers beyond the liver.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".