SU‐D‐211‐03: An Automated Inverse Planning Optimization Approach for Single‐ Fraction and Fractionated Radiosurgery Using Gamma Knife Perfexion
Bibliographic record
Abstract
PURPOSE: The purpose of this work is to develop an automated inverse planning approach to generate singe-fraction and fractionated stereotactic radiosurgery (SRS) treatment plans for Gamma Knife Perfexion. METHODS: Our automated approach consists of two steps: 1) a grassfire-based algorithm to carefully determine the isocentre locations; 2) a penalty-based optimization to find the optimal shot shapes and their intensities to minimize the deviation of the delivered dose from the objective dose in all structures. For single-fraction SRS, a margin-less approach was taken: conformity of dose to the gross tumor volume (GTV) with a steep dose fall-off was prioritized. For fractionated radiosurgery, dose homogeneity was given a higher priority since planning target volumes (PTV) were applied to account for daily setup variation, and these PTVs could overlap with organs-at-risk (OARs). The two-step approach was tested on seven clinical cases with PTV sizes of 0.5cm̂3-56.5cm̂3. In the tested cases, the PTV had 0%-38% overlap with OARs. RESULTS: For single-fraction SRS, the dose to 1mm̂3 brainstem was on average 0.24Gy (range: -2.4Gy to +2.0Gy) lower compared to manually-generated plans. Beam-on time varied with the number of isocentres, but on average was 33min longer than manually- generated plans. The optimization algorithm took 215min on average, while isocentre selection performed in <10s.For fractionated SRS, the average PTV coverage was V95=94.9% (range: 92.7%-97.6%) and the mean dose to 1 mm̂3 brainstem was 87.8% of the prescription dose (range: 35.4%- 108.8%). The mean beam-on time per fraction per dose-per-fraction was 4.8min/Gy (range: 0.9min/Gy-10.3min/Gy). We observed a tradeoff between conformity and OARs-sparing in both plans, and added sensitivity to isocentre locations in fractionated plans. In all the cases, GTV received the full prescription dose. CONCLUSIONS: The results indicated that automated inverse planning yields improved conformity and OAR-sparing for single- fraction SRS and is capable of generating homogeneous fractionated SRS. This work is partially funded by Elekta Instrument, AB, Stockholm, Sweden.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".