The effect of kV imaging dose on PTV and OAR planning constraints in lung SBRT using stereoscopic/monoscopic real‐time tumor‐monitoring system
Bibliographic record
Abstract
PURPOSE: Quantify the impact of additional imaging doses on clinical dose constraints during lung stereotactic body radiotherapy (SBRT) treatment utilizing stereoscopic/monoscopic real-time tumor monitoring. MATERIALS AND METHODS: Thirty lung SBRT patients treated with the volumetric arc therapy technique were randomly selected from the institutional clinical database. Contours of patients' and computed tomography data were extracted from the Eclipse treatment planning system, along with information regarding the treatment dose. Subsequently, patient-specific three-dimensional real-time imaging dose distributions were computed using a validated Monte Carlo simulation of the ExacTrac imaging. The 3D imaging dose was added to the treatment dose, and the influence of the imaging dose on clinical dose constraints was analyzed for planning target volume (PTV) and various organs at risk (OARs). RESULTS: Among the 30 patients, 14 patients exhibited one or more failed OAR constraints based solely on the treatment dose, resulting in a total of 24 constraint failures. The addition of the real-time imaging dose altered the pass/fail criteria for one OAR constraint and two PTV constraints. The change in constraint due to additional imaging dose relative to the prescription dose was less than 1% for all patients, except for one case, where it reached 1.9%, which had remained below the threshold of 5% recommended by AAPM TG-180 guidelines. Furthermore, the additional imaging dose relative to the treatment dose resulted in an increase in OAR constraints ranging from 0 to 27% (mean of 0.8%), with nine cases exceeding 5%. CONCLUSION: The current study represents the first attempt to investigate the impact of additional imaging doses on clinical planning constraints in real-time tumor monitoring during lung SBRT utilizing ExacTrac imaging system. The addition of an imaging dose will likely have minimal clinical impact.
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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 | 0.008 |
| 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".