Feasibility study of navigated endoscopy for the placement of high dose rate brachytherapy applicators in the esophagus and lung
Bibliographic record
Abstract
PURPOSE: To evaluate the electromagnetic (EM) tracking of endoscopes and applicators as a method of positioning a high dose rate (HDR) luminal applicator. METHOD: An anatomical phantom consisting of a rigid trachea and flexible esophagus was used to compare applicator placement measurements using EM tracking vs the traditional method using two-dimensional (2D) fluoroscopy and surface skin markers. The phantom included a tumor in the esophagus and several pairs of optically visible points inside the lumen that were used to simulate proximal and distal ends of tumors of varying lengths. The esophagus tumor and lung points were visible on a computed tomography (CT) image of the phantom, which was used as ground truth for the measurements. The EM tracking system was registered to the CT image using fiducial markers. A flexible endoscope was tracked using the EM system and the locations of the proximal and distal ends of the tumor identified and this position recorded. An EM-tracked applicator was then inserted and positioned relative to the tumor markings. The applicator path was mapped using the EM tracking. The gross tumor length (GTL) and the distance between the first dwell position and distal edge of tumor (offset) were measured using the EM tracking and 2D fluoroscopy methods and compared to the same measurements on the CT image. RESULTS: The errors in GTL using EM tracking were on average -0.5 ± 1.7 mm and 0.7 ± 3.6 mm for esophagus and lung measurements, similar to errors measured using the 2D fluoroscopy method of -0.9 ± 1.2 mm and 3.4 ± 4.4 mm. Offset measurements were slightly larger while using EM tracking relative to the fluoroscopy method but these were not statistically significant. CONCLUSIONS: Electromagnetic tracking for placement of lumen applicators is feasible and accurate. Tracking of the endoscope that is used to identify the proximal and distal ends of the tumor and of the applicator during insertion generates accurate three-dimensional measurements of the applicator path, GTL and offset. Guiding the placement of intraluminal applicators using EM navigation is potentially attractive for cases with complex insertions, such as those with nonlinear paths or multiple applicator insertions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".