How Low Should You Go: Choice of Minimum Dose Prescription in Cranial Radiosurgery
Bibliographic record
Abstract
BACKGROUND: In radiosurgery, the convention has been to prescribe radiation dose to a "covering isodose volume". This is presumed to be the minimum dose received by the entire tumor. Our purpose was to review our practice, assess different means of specifying a prescription isodose, and test how reliable they were in the face of various calculation methodologies. METHODS: Different minimum doses were calculated using three calculation methods for 20 brain targets and compared to the original physician intent. Monte Carlo (MC) calculations were run down to 2% (1 sigma) statistics. Voxel size depended on the imaging field of view and the calculation engine. For pencil beam convolution ("finite size pencil beam" - FSPB) calculations with or without heterogeneity correction (Methods 1-2), the calculation grid matched the CT scan resolution. For MC calculations (Method 3), the highest available in-plane resolution was 256 x 256 pixels. The median voxel volume was thus 0.58 mm(3) (0.47 to 0.95 mm(3)) for FSPB and 2.3 mm(3) (1.87 to 3.80) for MC. RESULTS: The absolute minimum target dose varied substantially between the three calculation methods - up to 25% difference between Methods 1 and 3. The differences were reduced when comparing near-minimum doses with absolute minimal volumes ΔV, DPTV-ΔV. The median difference in the isodose covering the PTV-0.03 cm(3) was 0% for Methods 1 and 2 and 3.6% for Methods 1 and 3. The median difference in the isodose covering the PTV-0.01 cm(3) was 0% for Methods 1 and 2 and 2.2% for Methods 1 and 3. In our data, the smaller the volume in which the minimum dose is calculated, the more sensitive this calculation was to dose calculation parameters. The standard deviation of the difference between physician intent and the isodose covering the PTV-0.01 cm(3) was 2.9% (range from -3.3% to 9.3%). CONCLUSION: In radiosurgery, absolute minimum doses are sensitive to changes in dose calculation grids and dose calculation algorithms. Based on our experience, standardizing dose prescription to the isodose volume covering the PTV-0.01 cm(3) or the PTV-0.03 cm(3) would have little impact on clinical practice and would be relatively insensitive to dose calculation parameters.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".