Feasibility and guidelines for the use of an injectable fiducial marker (BioXmark®) to improve target delineation in preclinical radiotherapy studies using mouse models.
Bibliographic record
Abstract
<ns3:p> Background: Preclinical models of radiotherapy (RT) response are vital for the continued success and evolution of RT in the treatment of cancer. The irradiation of tissues in mouse models necessitates high levels of precision and accuracy to recapitulate clinical exposures and limit adverse effects on animal welfare. This requirement has been met by technological advances in preclinical RT platforms established over the past decade. Small animal RT systems use onboard computed tomography (CT) imaging to delineate target volumes and have significantly refined radiobiology experiments with major 3Rs impacts. However, the CT imaging is limited by the differential attenuation of tissues resulting in poor contrast in soft tissues. Clinically, radio-opaque fiducial markers (FMs) are used to establish anatomical reference points during treatment planning to ensure accuracy beam targeting, this approach is yet to translate back preclinical models. Methods: We report on the use of a novel liquid FM BioXmark <ns3:sup>®</ns3:sup> developed by Nanovi A/S (Kongens Lyngby, Denmark) that can be used to improve the visualisation of soft tissue targets during beam targeting and minimise dose to surrounding organs at risk. We present descriptive protocols and methods for the use of BioXmark <ns3:sup>®</ns3:sup> in experimental male and female C57BL/6J mouse models. Results: These guidelines outline the optimum needle size for uptake (18-gauge) and injection (25- or 26-gauge) of BioXmark <ns3:sup>®</ns3:sup> for use in mouse models along with recommended injection volumes (10-20 µl) for visualisation on preclinical cone beam CT (CBCT) scans. Injection techniques include subcutaneous, intraperitoneal, intra-tumoral and prostate injections. Conclusions: The use of BioXmark <ns3:sup>®</ns3:sup> can help to standardise targeting methods, improve alignment in preclinical image-guided RT and significantly improve the welfare of experimental animals with the reduction of normal tissue exposure to RT. </ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".