Improving superficial target delineation in radiation therapy with endoscopic tracking and registration
Bibliographic record
Abstract
PURPOSE: Target delineation within volumetric imaging is a critical step in the planning process of intensity modulated radiation therapy. In endoluminal cancers, endoscopy often reveals superficial areas of visible disease beyond what is seen on volumetric imaging. Quantitatively relating these findings to the volumetric imaging is prone to human error during the recall and contouring of the target. We have developed a method to improve target delineation in the radiation therapy planning process by quantitatively registering endoscopic findings contours traced on endoscopic images to volumetric imaging. METHODS: Using electromagnetic sensors embedded in an endoscope, 2D endoscopic images were registered to computed tomography (CT) volumetric images by tracking the position and orientation of the endoscope relative to a CT image set. Regions-of-interest (ROI) in the 2D endoscopic view were delineated. A mesh created within the boundary of the ROI was projected onto the 3D image data, registering the ROI with the volumetric image. This 3D ROI was exported to clinical radiation treatment planning software. The precision and accuracy of the procedure was tested on two solid phantoms with superficial markings visible on both endoscopy and CT images. The first phantom was T-shaped tube with X-marks etched on the interior. The second phantom was an anatomically correct skull phantom with a phantom superficial lesion placed on the pharyngeal surface. Markings were contoured on the endoscope images and compared with contours delineated in the treatment planning system based on the CT images. Clinical feasibility was tested on three patients with early stage glottic cancer. Image-based rendering using manually identified landmarks was used to improve the registration. RESULTS: Using the T-shaped phantom with X-markings, the 2D to 3D registration accuracy was 1.5-3.5 mm, depending on the endoscope position relative to the markings. Intraobserver standard variation was 0.5 mm. Rotational accuracy was within 2°. Using the skull phantom, registration accuracy was assessed by calculating the average surface minimum distance between the endoscopy and treatment planning contours. The average surface distance was 0.92 mm with 93% of all points in the 2D-endoscopy ROI within 1.5 mm of any point within the ROI contoured in the treatment planning software. This accuracy is limited by the CT imaging resolution and the electromagnetic (EM) sensor accuracy. The clinical testing demonstrated that endoscopic contouring is feasible. With registration based on em tracking only, accuracy was 5.6-8.4 mm. Image-based registration reduced this error to less than 3.5 mm and enabled endoscopic contouring in all cases. CONCLUSIONS: Registration of contours generated on 2D endoscopic images to 3D planning space is feasible, with accuracy smaller than typical set-up margins. Used in addition to standard 3D contouring methods in radiation planning, the technology may improve gross tumour volume (GTV) delineation for superficial tumors in luminal sites that are only visible in endoscopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".