Percutaneous Liver Fiducial Implants: Techniques, Materials and Complications
Bibliographic record
Abstract
Stereotactic body radiation therapy (SBRT) is a radiotherapy technique that utilizes precise targeting to deliver high doses of tumoricidal radiation in the course of a small number of treatment sessions. SBRT is used to treat a variety of primary and metastatic tumours of the lung, liver, pancreas, kidney, spine and prostate. These treatments are already standard for medically inoperable early lung cancer and indications are evolving for other disease sites. In SBRT of liver lesions (Dawood et al., 2009; Lo et al., 2010), tumour-targeting accuracy is crucial given the radiosensitive nature of the liver, frequent proximity of the tumour to the small bowel and significant movement of the liver with breathing. Reduction of normal tissue irradiation requires the radiation to be image-guided (as opposed to relying on skin marks or body casts). This image-guidance is typically accomplished through visualization of a surrogate to the tumour. The surrogate can be the whole liver, the diaphragm or an implanted marker (Wunderink et al., 2010). Implanted markers have the advantage of being visible on planar x-ray images and fluoroscopy loops. If large enough, they can also be seen on images produced with the treatment beam. The details of the implementation of fiducial imaging vary with different radiotherapy devices but typically images of the fiducials are correlated to the position of the chest wall at different points in the breathing cycles. This gives the user knowledge of the displacement of the tumour (assumed to be at a fixed distance from the fiducials). One can then choose to turn on the beam in a specific phase of the breathing cycle or have the radiation delivery device track the movement of the tumour. As an example, the Cyberknife system acquires up to 15 pairs of stereoscopic x-rays just before treatment. Fiducials are automatically detected by the system’s image analysis software. The 3D position of the fiducials is then correlated to the position of chest wall as recorded by the movement of 3 lights on the patient’s chest. The robot on which the radiation source is mounted can then use the model correlating chest wall motion and fiducial motion to mimic the movement of the tumour while the patient breathes (Figure 1). In this process, there is continuous monitoring (via cameras) of the chest wall but only intermittently imaging of the fiducials with stereoscopic x-rays. These additional x-rays are used to verify and update the correlation model.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".