Sci-PM Thurs - 08: Development of a lung tumour model for validating three-dimensional thoracoscopic ultrasound imaging
Bibliographic record
Abstract
Introduction: Potential minimally invasive lung cancer therapies such as brachytherapy will require intraoperative imaging for both instrument tracking and tumour volume measurements for radiation dose planning. Ultrasound (US) is the modality of choice because it is inexpensive, real-time, portable, and non-invasive to the patient. Objectives: We have developed a lung tumour model, using excised porcine lung and agar tumours, to provide a means of verifying volume measurements of 3D US images in ex vivo lung tissue. Methods: Spherical tumours were made from agar with diameters of 9.5mm to 25.4mm. The tumours were inserted through incisions on the underside of the excised porcine lung. The lung was placed in a box with ports and the thoracoscopic US probe was inserted through a port for imaging. One observer measured the tumour image volumes five times, once every two days, using a radial segmentation algorithm with an interslice thickness of three degrees. Results: Both the coefficient of variation (COV) and percent difference decreased as the tumour size increased. The average COV and percent difference were 11.2% and 12.9%, respectively. Conclusions: 3D Thoracoscopic US can be used accurately and reproducibly to measure tumour volumes in vitro.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".