Body contour adaptation for weight‐loss and bolus for head and neck radiotherapy on Ethos version 2.0 and HyperSight: Synthetic CT versus direct calculation
Bibliographic record
Abstract
Abstract Purpose In radiotherapy, body contour inaccuracies may compromise the delineation of adjacent structures and affect calculated dose. Here, we evaluate the un‐editable body contours auto‐generated by Ethos versions 1.0 (v1) and 2.0 (v2) treatment planning softwares for two simulated cases: weight‐loss and bolus application, particularly important for head and neck radiotherapy patients. Methods A 3D‐printed target structure was secured to the neck of an anthropomorphic phantom and sequentially covered with silicone boluses of uniform thickness, providing cases for bolus application (0.5 and 1 cm) and weight‐loss (2.0, 1.5, 1.0, 0.5, and 0 cm). HyperSight CBCT images of the phantom were acquired to simulate the online adaptation process. Baseline body contours were manually produced and compared to those auto‐generated in Ethos v1 (synthetic CTs) and Ethos v2 (synthetic CTs and direct calculation on HyperSight CBCTs). Additionally, the target volume D95% dose metric for weight‐loss adapted plans generated by the Ethos v2 were analyzed as a function of surface layer thickness. Results The Ethos v1 body contour did not adapt adequately for the weight‐loss image set [mean absolute volume deviation from baseline (MAD) = 205 cm 3 ]. The weight‐loss synthetic CT and HyperSight CBCT volumes in Ethos v2 were comparable to manually generated contours (MAD = 34 and 46 cm 3 , respectively); however, the bolus Hypersight CBCT body contour intersected the outer edge of the phantom (MAD = 157 cm 3 ). The D95% deviation from the planned dose decreased by up to 10% when using the Ethos v2 adapted plan for the weight‐loss scenario. Conclusion Contours in Ethos v1 rely on reference contours and deformable registration algorithms, whereas Ethos v2 does not. Hence, Ethos v2 is preferred for cases involving weight change. A tight‐fitted air gap‐free bolus is critical for achieving accurate body contours for Ethos v2 Hypersight CBCTs.
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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) | 0.000 | 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) | 0.000 | 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".