Dosimetric evaluation of Ethos 2.0 high‐fidelity mode for single‐isocenter SRS
Bibliographic record
Abstract
PURPOSE: This study evaluates the impact of the Ethos 2.0 high-fidelity (HF) mode on single-isocenter, stereotactic radiosurgery (SRS) planning workflows. By comparing different planning templates, we assess the effects of HF mode and control rings (R) on plan quality, aiming to optimize treatment for patients with brain metastases. METHODS: A cohort of 45 patients with brain metastases was divided into a tuning (n = 15) and a validation set (n = 30). Four planning templates were evaluated: HFonRon, HFonRoff, HFoffRon, and HFoffRoff. Plans were generated using the Ethos Intelligent Optimization Engine (IOE v02.00.10), and all patients were prescribed 30 Gy in 5 fractions using 2 mm PTV margins. Plan quality was evaluated using target coverage (PTV V100%), normal brain dose (Brain-PTV V30Gy), Paddick conformity index (CI), and Falloff index. Plan complexity was evaluated using total MU's on a per-plan basis across the four templates. RESULTS: The HFonRon template produced better quality plans, achieving significant improvements in normal tissue sparing (p < 0.0001), CI (p < 0.0001), and Falloff index (p < 0.0001) compared to other templates. All templates provided clinically acceptable target coverage. In the HFoffRoff configuration, 97.5 % of targets met the coverage criterion (V100% > 95%). The other setups achieved coverage in 99.2% (HFoffRon), 96.6% (HFonRoff), and 98.3% (HFonRon) of targets, respectively. Plan complexity decreased significantly (p < 0.0001) when enabling HF mode compared to both HF disabled templates CONCLUSIONS: His study shows that combining Ethos 2.0's high-fidelity mode with user-defined control rings yields superior single-isocenter SRS plans compared with planning strategies that omit one or both features. This approach improves dose conformity, reduces normal tissue exposure, and decreases plan complexity through semi-automated, template-based planning. These findings suggest that HF mode can be a valuable tool in clinical practice for optimizing SRS treatments for patients with brain metastases.
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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 | 0.002 |
| 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".