Comparison of visibility of iodinated hydrogel and gadolinium-modified hyaluronic acid spacer gels on computed tomography and onboard imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In accelerated partial breast irradiation (APBI), accurate definition of the tumour bed is crucial to reduce the risk of local recurrence and the volume of healthy tissue irradiated. Recently, hydrogels have been proposed to improve visibility of the lumpectomy cavity for APBI. The aim of this study was to alter two commercially available hyaluronic acid (HA) gels, with gadopentenate dimeglumine (GD), a magnetic resonance imaging (MRI) contrast agent. We hypothesize that after injection in the surgical cavity, the mixtures will be visible with computed tomography (CT) for improved treatment planning, cone-beam CT (CBCT) for improved patient setup and planar kilovoltage (kV) x-ray for real-time tracking during treatment. MATERIALS AND METHODS: study, GD was mixed with the two HA gels, and 1 mL of each mixture was injected into fatty and muscular tissue of a pork phantom. Visibility with CT, CBCT and planar x-ray imaging was assessed. Contrast-to-noise ratios (CNR) were measured and compared to commercially available iodinated polyethylene glycol (PEG). RESULTS: The gel mixtures showed increased visibility over HA gels without GD. When comparing CNR of the gel mixtures to that of iodinated PEG on CT, there was a 4-fold increase in muscle for both mixtures and a 1.6-fold to 3.6-fold increase in fat, depending on the HA gel. Gel mixtures showed better visibility with planar kV imaging over iodinated PEG. CONCLUSION: Addition of GD to HA gels increases visibility with CT, CBCT and planar x-ray imaging, indicating potential for improved delineation and positioning in APBI.
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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 | 0.001 | 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) | 0.001 | 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".