Simultaneous Infield Boost With Helical Tomotherapy for Patients With 1 to 3 Brain Metastases
Bibliographic record
Abstract
OBJECTIVE: We sought to model the feasibility of a simultaneous in field boost (SIB) to individual brain metastases during a course of whole brain radiotherapy (WBXRT) using helical tomotherapy (HT) intensity-modulated radiation therapy. PATIENTS AND METHODS: Planning computed tomography data from 14 patients with 1 to 3 brain metastases were used to model an intralesional SIB delivery that yielded a total intralesional dose of 60 Gy with a surrounding whole brain dose of 30 Gy (designed to be isoeffective to WBXRT of 30 Gy with an 18 Gy in 1 fraction radiosurgery boost). Accuracy of treatment of a phantom on the HT unit was measured. Comparisons of HT delivery versus a conventional stereotactic radiotherapy technique for a particularly challenging simulated anatomy were made. RESULTS: In all cases, SIB to 60 Gy with WBXRT to 30 Gy was possible while maintaining critical structures below assigned dose limits. Estimated radiation delivery time for the SIB treatment was approximately 10 minutes per fraction. Planning and treatment of the head phantom was associated with an overall accuracy of 2 mm. Comparison to conventional noncoplanar arc fractionated stereotactic radiotherapy plan demonstrated similar target coverage and improved critical tissue sparing even for a challenging anatomy with multiple lesions in the same plane as the optic apparatus. CONCLUSIONS: Based on this study, use of an image guided SIB using HT seemed feasible and a phase I trial initiated at our institution is described. Potential advantages of this approach include frameless stereotaxis through daily megavoltage computed tomography localization, more efficient use of resources and exploitation of radiobiologic advantages of fractionation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".