Application value of liquid fiducial marker in image-guided radiotherapy
Bibliographic record
Abstract
This study was conducted to evaluate the application value of a degradable liquid fiducial marker (LFM) in image-guided radiotherapy. In vitro experiment: using a solid fiducial marker (SFM) as a reference, the visibility, artifact, and optimal injection volume of an LFM under different cone beam CT tube voltage conditions were evaluated. In vivo experiment: using the SFM as a reference, the stability and degradation status of the LFM in nude mice were evaluated. Nude mice implanted with tumor cells were randomly divided into four groups: single fraction radiotherapy group (16 Gy/fraction) without LFM injection, single fraction radiotherapy group (16 Gy/fraction) with LFM injection, 2 fractions radiotherapy group (8 Gy/fraction) with LFM injection, and 4 fractions radiotherapy group (4 Gy/fraction) with LFM injection. The impact of LFM on tumor growth was evaluated based on the irradiation results. Compared with SFM, the LFM artifacts were significantly smaller (all p<0.05), and the visibility met the clinical differentiation requirements. The best imaging quality was achieved when the injection volume was 10 μL. The displacement of the LFM centroid relative to the spinal cord in the nude mice was significantly greater than that of the gold fiducial marker ((0.22 ± 0.03) mm vs. (0.17 ± 0.02) mm, p<0.05); however, it was always smaller than a pixel size. The results indicated good stability. The actual degradation rate of the LFM was highly consistent with the theoretical degradation rate. The LFM had a relatively smaller impact on tumor growth in the single fraction radiotherapy group but a greater impact in the fractional radiotherapy groups. LFMs have certain clinical applications and promotional value, and they are expected to replace SFMs in the future.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".