An Evaluation of Total Internal Motions of Locally Advanced Pancreatic Cancer during SABR Using Calypso® Extracranial Tracking, and Its Possible Clinical Impact on Motion Management
Bibliographic record
Abstract
(1) Background: the aims of this study were to determine the total extent of pancreatic cancer’s internal motions, using Calypso® extracranial tracking, and to indicate possible clinical advantages of continuous intrafractional fiducial-based tumor motion tracking during SABR. (2) Methods: thirty-four patients were treated with SABR for LAPC using Calypso® for motion management. Planning MSCTs in FB and DBH, and 4D-CTs were performed. Using data from Calypso® and 4D-CTs, the movements of the lesions in the CC, AP and LR directions, as well as the volumes of the 4D-CT-based ITV and the volumes of the Calypso®-based ITV were compared. (3) Results: significantly larger medians of tumor excursions were found with Calypso® than with 4D-CT: CC: 29 mm (p < 0.001); AP: 14 mm (p < 0.001) and LR: 11 mm (p < 0.039). The median volume of the Calypso®-based ITV was significantly larger than that of the 4D-CT based ITV (p < 0.001). (4) Conclusion: beside known respiratory-induced internal motions, pancreatic cancer seems to have significant additional motions which should be considered during respiratory motion management. Only direct and continuous intrafractional fiducial-based motion tracking seems to provide complete coverage of the target lesion with the prescribed isodose, which could allow for safe tumor dose escalation.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".