In‐vivo quality assurance of dynamic tumor tracking (DTT) for liver SABR using EPID images
Bibliographic record
Abstract
Abstract Purpose To assess dynamic tumor tracking (DTT) target localization uncertainty for in‐vivo marker‐based stereotactic ablative radiotherapy (SABR) treatments of the liver using electronic‐portal‐imaging‐device (EPID) images. The Planning Target Volume (PTV) margin contribution for DTT is estimated. Methods Phantom and patient EPID images were acquired during non‐coplanar 3DCRT‐DTT delivered on a Vero4DRT linac. A chain‐code algorithm was applied to detect Multileaf Collimator (MLC)‐defined radiation field edges. Gold‐seed markers were detected using a connected neighbor algorithm. For each EPID image, the absolute differences between the measured center‐of‐mass (COM) of the markers relative to the aperture‐center (Tracking Error, (ET)) was reported in pan, tilt, and 2D‐vector directions at the isocenter‐plane. Phantom study An acrylic cube phantom implanted with gold‐seed markers was irradiated with non‐coplanar 3DCRT‐DTT beams and EPID images collected. Patient Study: Eight liver SABR patients were treated with non‐coplanar 3DCRT‐DTT beams. All patients had three to four implanted gold‐markers. In‐vivo EPID images were analyzed. Results Phantom Study: On the 125 EPID images collected, 100% of the markers were identified. The average ± SD of ET were 0.24 ± 0.21, 0.47 ± 0.38, and 0.58 ± 0.37 mm in pan, tilt and 2D directions, respectively. Patient Study: Of the 1430 EPID patient images acquired, 78% had detectable markers. Over all patients, the average ± SD of ET was 0.33 ± 0.41 mm in pan, 0.63 ± 0.75 mm in tilt and 0.77 ± 0.80 mm in 2D directions The random 2D‐error, σ, for all patients was 0.79 mm and the systematic 2D‐error, Σ, was 0.20 mm. Using the Van Herk margin formula 1.1 mm planning target margin can represent the marker based DTT uncertainty. Conclusions Marker‐based DTT uncertainty can be evaluated in‐vivo on a field‐by‐field basis using EPID images. This information can contribute to PTV margin calculations for DTT.
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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 | 0.006 |
| 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".