Migration of Calypso beacon transponders for hepatic stereotactic body radiotherapy: a report of two cases
Bibliographic record
Abstract
Background: The Calypso 4-dimensional Localization System allows the delivery of high-dose of radiation to a target guided by the implanted transponders. Calypso beacons are used for prostate and liver tumors treated with stereotactic body radiation therapy (SBRT). Several risks associated with this procedure have been previously observed. Here, we report on two cases where Calypso soft tissue transponders migrated to the lung shortly after implantation in liver. Case Description: Two male patients with hepatocellular carcinoma (HCC) underwent insertion of Calypso beacons in liver under image-guidance in preparation for SBRT. Post-procedure images confirmed the presence of the transponders within the liver. However, few days after implant, further imaging revealed a missing marker, in each patient, that had migrated to the right lung. Patients were asymptomatic and SBRT was delivered uneventfully. Conclusions: This is the first report of migration of Calypso beacons from liver to lung. In order to reduce the risk of migration, a Doppler ultrasound (US) prior to insertion could be performed to ensure that the transponders are at a safe distance from blood vessels. Anchored Calypso beacons, currently approved for insertion in the lung, could be tested as a suitable alternative to soft tissue beacons with a lower risk of migration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".