Dose calculation accuracy of clinical radiotherapy plans using next generation cone beam computed tomography imaging technology
Bibliographic record
Abstract
Background and purpose: Next generation cone beam computed tomography (CBCT) technology has shown improved Hounsfield unit accuracy over standard CBCT, and comparable to that of fan beam CT simulators (CTsim), potentially allowing direct dose calculation on CBCT. In this study, we evaluated the dose calculation accuracy of clinical treatment plans calculated using this technology, compared with standard CBCT. Materials and methods: Thirty subjects with thoracic or upper abdominal cancer were imaged on CTsim, standard CBCT and next generation CBCT, and treated using breath hold techniques. Both CBCT image sets were rigidly registered to CTsim, and clinical treatment plans were forward calculated on all images. 3D Gamma analysis was used to evaluate CBCT dose distributions relative to CTsim, and DVH analysis compared PTV and OAR dose metrics. Results: The median (±IQR) 3 %/3 mm gamma pass rate was 96.7 ± 3.1 % for next generation CBCT and 93.3 ± 14.0 % for standard CBCT. Next generation CBCT gamma scores were statistically significantly higher than standard CBCT for all gamma criteria. Median DVH metrics were within ± 2.3 % and ± 2.7 % of the corresponding values from CTsim, for next generation and standard CBCT respectively. Standard CBCT showed an underestimation of ipsilateral lung dose for breast subjects, while next generation CBCT did not. Conclusions: Next generation CBCT allows for good average DVH agreement with CTsim, and improved dose calculation accuracy over standard CBCT across 3D dose distributions. While further clinical investigations are warranted, this technology may allow for the use of CBCT in direct-dose calculation in adaptive radiotherapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".