SU‐GG‐T‐341: Initial Results of Real‐Time External Surface Motion and Dose Monitoring Study for Lung Patients
Bibliographic record
Abstract
Purpose: To describe results from an ongoing clinical trial that aims to evaluate the potential of the RADPOS system, which combines a MOSFET dosimeter and electromagnetic positioning sensor, for applications in external beam treatments for lung cancer patients. Method and Materials: Measurements were done at the time of each patient's 4DCT and throughout the course of treatment. Each day, three RADPOS sensors were positioned at marked points on the patient's chest and abdomen while a fourth detector was placed on the CT or treatment couch for reference. Position coordinates of the sensors and dose information can be read in real‐time, but for these trials the total dose was read after each treatment fraction. Results: Measurements have been completed on ten patients during 7–14 fractions each. The standard deviation of the average dose measured at each point ranged from 3.0–13.7 cGy at CT zero and 2.5–11.1 cGy at the site of the tumour. Large differences were sometimes seen between data collected during the 4DCT and treatment fractions. Most patients settled into a more consistent breathing pattern as treatment progressed, with maximum interfraction variations in average amplitude and period between 0.9–3.5 mm and 0.2–1.8 s during treatment. A cross‐correlation analysis comparing the displacements measured simultaneously at the three locations found that correlation varied between patients, as no two detectors were consistently the most correlated. The magnitude of the correlation coefficients also varied greatly, ranging from ρ=0.13–0.24 for Patient G and from ρ=0.74–0.81 for Patient E. Conclusion: RADPOS system can provide real‐time feedback regarding motion due to breathing, coughing or other patient movement. Variations in breathing patterns are patient‐specific and should be monitored to ensure accurate positioning and treatment delivery. Acknowledgements: This project is supported by grants from HTX and ORCC Foundation. Financial and technical support from Best Medical Canada is also acknowledged.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".