Dosimetric Comparison of Intensity-Modulated Radiosurgery and Helical Tomotherapy for the Treatment of Multiple Intracranial Metastases
Bibliographic record
Abstract
The purpose of this study was to evaluate the dosimetry of single fraction, single-isocenter intensity-modulated radiosurgery (IMRS) plans for multiple intracranial metastases and to compare Helical Tomotherapy (HT). Ten treatment plans with 3-6 brain metastases treated with IMRS were re-planned with HT. The mean number of lesions was 5 and mean PTV 22 cm(3). The prescribed dose was 16-20 Gy. The mean V100% was similar for IMRS and HT, and the mean conformity index was 1.4, mean Paddick confirmity index was 0.7, and mean MDPD was 1.1 for both. The mean gradient index was similar for both. The mean 50% _isodose volume was 179.2 cm(3) for IMRS and 277.0 cm(3) for HT (p=0.01). The mean maximum doses to organs at risk were lower for IMRS except brainstem and right optic nerve. For brain, the integral dose was 5.1 and 6.8 Gy-kg (p<0.001) and mean dose 4.0 and 5.4 Gy (p<0.001) for IMRS and HT, respectively. The mean treatment times were 23 (IMRS) and 41 (HT) minutes. Conformity and homogeneity indices were equivalent and sparing of the organs at risk was clinically acceptable for both IMRS and HT. Though the gradient index was similar for IMRS and HT, the mean 50% isodose volume and integral dose to normal brain were lower for IMRS as was treatment time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".