Monte Carlo study on the sensitivity of prompt gamma imaging to proton range variations due to interfractional changes in prostate cancer patients
Bibliographic record
Abstract
Proton range verification based on prompt gamma imaging is increasingly considered in proton therapy. Tissue heterogeneity normal to the beam direction or near the end of range may considerably degrade the ability of prompt gamma imaging to detect proton range shifts. The goal of this study was to systematically investigate the accuracy and precision of range detection from prompt gamma emission profiles for various fractions for intensity modulated proton therapy of prostate cancer, using a comprehensive clinical dataset of 15 different CT scans for 5 patients. Monte Carlo simulations using Geant4 were performed to generate spot-by-spot dose distributions and prompt gamma emission profiles for prostate treatment plans. The prompt gammas were scored at their point of emission. Three CT scans of the same patient were used to evaluate the impact of inter-fractional changes on proton range. The range shifts deduced from the comparison of prompt gamma emission profiles in the planning CT and subsequent CTs were then correlated to the corresponding range shifts deduced from the dose distributions for individual pencil beams. The distributions of range shift differences between prompt gamma and dose were evaluated in terms of precision (defined as half the 95% inter-percentile range IPR) and accuracy (median). In total about 1700 individual proton pencil beams were investigated. The IPR of the relative range shift differences between the dose profiles and the prompt gamma profiles varied between ±1.4 mm and ±2.9 mm when using the more robust profile shifting analysis. The median was found smaller than 1 mm. Methods to identify and reject unreliable spots for range verification due to range mixing were derived and resulted in an average 10% spot rejection, clearly improving the prompt gamma-dose correlation. This work supports that prompt gamma imaging can offer a reliable indicator of range changes due to anatomical variations and tissue heterogeneity in scanning proton treatment of prostate cancer patients when considering prompt gamma emission profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".