Patient-Specific Planning Target Volume Margins for Liver Stereotactic Robotic Radiosurgery
Bibliographic record
Abstract
Purpose: To create analytical tools for a proposed clinical workflow, which implements patientspecific planning target volume (PTV) margins for liver radiation therapy treatments.Methods and Materials: Treatment log files are analyzed for liver treatments to assess tumour motion-tracking accuracy.A uniform PTV margin is estimated that considers motion-tracking errors and deformations, provided that the impact of uncorrected rotations is minimized.A supervised machine learning algorithm employing retrospective data, which emulates a dry-run session prior to treatment planning, is used to investigate if motion-tracking errors are less than 2 mm, and consequently, the standard PTV margins can be reduced by 2 mm.For a safer implementation in the clinic, we employ a warning system that quantifies the probability of a geographic miss if the PTV margin is reduced for every subsequent fraction.A dosimetric analytical tool is proposed to retrospectively assess the dose to a target against different types of delivery errors.The tool is validated by radiochromic film measurements for two very different types of treatments, liver and trigeminal neuralgia (a cranial nerve disorder).Case studies are conducted to access the suitability of a selected PTV margin based on the geometrical and dosimetric coverage of targets.The range of rotations that can be safely allowed for trigeminal neuralgia treatments is quantified since rotational corrections cannot be applied by the system for this specific disease site.Results: Isotropic 4 mm PTV margins are sufficient to account for tracking errors and deformations for 95% of patients.For patient-specific PTV margins, the accuracy of predicting if motion-tracking errors are less than 2 mm is 0.84 ± 0.06 using 5-fold cross-validation.Using the warning system, 11 out of 64 cases predicted to be treated with 2 mm reduced PTV margins might Abstract ii require replanning, but for each fraction they have more than 96% of target(s) encompassed by the reduced PTV.For experiments with different types of geometrical errors the dose measurements with radiochromic film agree well (2%/2 mm level) with the dose distributions estimated using the dosimetric analytical tool.Dose to targets considering delivery errors can be significantly improved if treatments are planned following certain guidelines.For trigeminal neuralgia treatments, target rotations of up to 1° can be safe for some patients.Conclusions: For treatment adaptation it is feasible to implement patient-specific PTV margins in the clinic, assisted with an early-warning system and dosimetric analytical tool to warn of a potential geographic miss and underdosing of target(s).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".