Comparison of proton therapy treatment planning for head tumors with a pencil beam algorithm on dual and single energy CT images
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Dual energy CT (DECT) has recently been proposed as an improvement over single energy CT (SECT) for stopping power ratio (SPR) estimation for proton therapy treatment planning (TP), thereby potentially reducing range uncertainties. Published literature investigated phantoms. This study aims at performing proton therapy TP on SECT and DECT head images of the same patients and at evaluating whether the reported improved DECT SPR accuracy translates into clinically relevant range shifts in clinical head treatment scenarios. METHODS: Two phantoms were scanned at a last generation dual source DECT scanner at 90 and 150 kVp with Sn filtration. The first phantom (Gammex phantom) was used to calibrate the scanner in terms of SPR while the second served as evaluation (CIRS phantom). DECT images of five head trauma patients were used as surrogate cancer patient images for TP of proton therapy. Pencil beam algorithm based TP was performed on SECT and DECT images and the dose distributions corresponding to the optimized proton plans were calculated using a Monte Carlo (MC) simulation platform using the same patient geometry for both plans obtained from conversion of the 150 kVp images. Range shifts between the MC dose distributions from SECT and DECT plans were assessed using 2D range maps. RESULTS: SPR root mean square errors (RMSEs) for the inserts of the Gammex phantom were 1.9%, 1.8%, and 1.2% for SECT phantom calibration (SECTphantom), SECT stoichiometric calibration (SECTstoichiometric), and DECT calibration, respectively. For the CIRS phantom, these were 3.6%, 1.6%, and 1.0%. When investigating patient anatomy, group median range differences of up to -1.4% were observed for head cases when comparing SECTstoichiometric with DECT. For this calibration the 25th and 75th percentiles varied from -2% to 0% across the five patients. The group median was found to be limited to 0.5% when using SECTphantom and the 25th and 75th percentiles varied from -1% to 2%. CONCLUSIONS: Proton therapy TP using a pencil beam algorithm and DECT images was performed for the first time. Given that the DECT accuracy as evaluated by two phantoms was 1.2% and 1.0% RMSE, it is questionable whether the range differences reported here are significant.
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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 | 0.000 | 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 it