Direct-to-Treatment Adaptive Radiation Therapy: Live Planning of Spine Metastases Using Novel Cone Beam Computed Tomography
Bibliographic record
Abstract
PURPOSE: Cone beam computed tomography (CBCT)-based online adaptive radiation therapy is carried out using a synthetic CT (sCT) created through deformable registration between the patient-specific fan-beam CT, fan-beam computed tomography (FBCT), and daily CBCT. Ethos 2.0 allows for plan calculation directly on HyperSight CBCT and uses artificial intelligence-informed tools for daily contouring without the use of a priori information. This breaks an important link between daily adaptive sessions and initial reference plan preparation. This study explores adaptive radiation therapy for spine metastases without prior patient-specific imaging or treatment planning. We hypothesize that adaptive plans can be created when patient-specific positioning and anatomy is incorporated only once the patient has arrived at the treatment unit. METHODS AND MATERIALS: An Ethos 2.0 emulator was used to create initial reference plans on 10 patient-specific FBCTs. Reference plans were also created using FBCTs of (1) a library patient with clinically acceptable contours and (2) a water-equivalent phantom with placeholder contours. Adaptive sessions were simulated for each patient using the 3 different starting points. Resulting adaptive plans were compared with determine the significance of patient-specific information prior to the start of treatment. RESULTS: The library patient and phantom reference plans did not generate adaptive plans that differed significantly from the standard workflow for all clinical constraints for target coverage and organ at risk sparing (P > .2). Gamma comparison between the 3 adaptive plans for each patient (3%/3 mm) demonstrated overall similarity of dose distributions (pass rate > 95%), for all but 2 cases. Failures occurred mainly in low-dose regions, highlighting difference in fluence used to achieve the same clinical goals. CONCLUSIONS: This study confirmed feasibility of a procedure for treatment of spine metastases that does not rely on previously acquired patient-specific imaging, contours or plan. Reference-free direct-to-treatment workflows are possible and can condense a multistep process to a single location with dedicated resources.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".