Sci‐Thur PM ‐ Colourful Interactions: Highlights 06: Patient‐specific calibration of cone‐beam computed tomographic (CBCT) image sets for on‐line dose tracking and plan assessment
Bibliographic record
Abstract
Purpose: To develop a radiotherapy dose tracking and plan evaluation technique using cone‐beam computed tomography (CBCT) images. Methods: We developed a patient‐specific method of calibrating CBCT image sets for dose calculation. The planning CT was first registered with the CBCT using deformable image registration (DIR). A scatter plot was generated between the CT numbers of the planning CT and CBCT for each slice. The CBCT calibration curve was obtained by least‐square fitting of the data, and applied to each CBCT slice. The calibrated CBCT was then merged with original planning CT to extend the small field of view of CBCT. Finally, the treatment plan was copied to the merged CT for dose tracking and plan evaluation. The proposed patient‐specific calibration method was also compared to two methods proposed in literature. To evaluate the accuracy of each technique, 15 head‐and‐neck patients requiring plan adaptation were arbitrarily selected from our institution. The original plan was calculated on each method's data set, including a second planning CT acquired within 48 hours of the CBCT (serving as gold standard). Clinically relevant dose metrics and 3D gamma analysis of dose distributions were compared between the different techniques. Results: Compared to the gold standard of using planning CTs, the patient‐specific CBCT calibration method was shown to provide promising results with gamma pass rates above 95% and average dose metric agreement within 2.5%. Conclusions: The patient‐specific CBCT calibration method could potentially be used for on‐line dose tracking and plan evaluation, without requiring a re‐planning CT session.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.042 |
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".