Quantification of the inherent radiopacity of glass microspheres for precision dosimetry in yttrium-90 radioembolization
Bibliographic record
Abstract
Abstract Introduction:Transarterial radioembolization is a treatment for nonresectable, hypervascular liver tumours where yttrium-90-infused microspheres are administered through the arterial vasculature of the liver to selectively target liver tumours. Compared to conventional PET and SPECT imaging, post-procedural CT imaging has the potential to provide superior spatial resolution imaging of microsphere distributions and improve dosimetry estimates. In this paper, we describe a methodology to quantify the inherent radiopacity of glass microspheres using CT. This methodology produces a calibration curve that relates microsphere concentration within a CT voxel to the corresponding change of Hounsfield unit for that voxel. Methods: The radiopaque microspheres under investigation are composed of proprietery blends of yttrium-strontium-gallium-silicate oxide glass similar in size and density to TheraSphere ® microspheres. Tissue-equivalent phantoms were designed to determine CT voxel enhancement from uniformly distributed microspheres. Phantoms were imaged with a 128-slice CT scanner to determine the average Hounsfield unit value and with brightfield microscopy to determine the corresponding microsphere concentrations. Results: Hounsfield units (HU) and microsphere concentration (MS/mL) are positively correlated (r 2 ≥ 0.930) over a range of CT acquisition parameters. Calibration curve slopes (sensitivities) range from 2.22 × 10 −4 to 3.12 × 10 −4 HU/MS/ml. Minimum detectable limits are between 1.83 × 10 5 and 2.54 × 10 5 MS mL −1 . The application of this proposed methodology to recently developed microsphere formulations shows an improvement in correlation (r 2 ≥ 0.995), sensitivity (7.53 × 10 −4 HU/MS/ml), and minimum detectability (5.39 × 10 4 MS ml −1 ). Conclusion: CT has the potential to quantify the radiation dose from the infusion of microspheres for more accurate dosimetry in radioembolization. This finding may improve our understanding of the relationship between absorbed dose and tumour response, which could ultimately translate into improved patient outcomes. Optimization of the prototype microsphere composition to maximize its inherent radiopacity will be an important step in realizing this goal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".