Feasibility, detectability and clinical experience with platinum fiducial seeds for MRI/CT fusion and real-time tumor tracking during CyberKnife<sup>®</sup> stereotactic ablative radiotherapy.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this study is to review our experience with platinum fiducials in terms of feasibility of placement and detectability by both MRI and orthogonal x-ray images used in robotic SABR.Materials and Methods: 29 consecutive SABR patients (30 tumors) treated using fiducial tracking between January 2011 and February 2012 were reviewed. A total of 108 fiducials implanted in or around various tumor sites were identified. The pixel value contrast (PVC) of fiducials seen on MRI mages and treatment unit x-ray images of patients and phantoms were analysed. RESULTS: Migration rates were similar for PS versus GS and GC (6.2%). No difference was noted between the mean PVC in cirrhotic versus non-cirrhotic liver (60.4 vs. 47.9; p = 0.074). MRI sequences for tumors in the liver and other organs revealed a mean PVC for platinum superior to that of gold (p<0.001). No PVC difference was seen between gold and platinum on analysis of the treatment unit x-rays. CONCLUSION: Platinum seeds provide a superior detectability in comparison to gold seeds or coils on MRI images and are detected equally well by an image guidance system using orthogonal x-rays, making them a better choice for fiducial-based CT-MRI registration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".