The influence of respiratory motion on dose delivery in a mouse lung tumour irradiation using the 4D MOBY phantom
Bibliographic record
Abstract
OBJECTIVE: During precision irradiation of a preclinical lung tumour model, the tumour is subject to breathing motion and it can partially move out of the irradiation field. This work aimed to perform a quantitative analysis of the impact of respiratory motion on a mouse lung tumour irradiation with small fields. METHODS: A four-dimensional digital mouse whole body phantom (MOBY) with a virtual 4-mm spherical lung tumour at different locations in both lungs is used to simulate a breathing anaesthetized mouse in different breathing phases representing a full breathing cycle. The breathing curve is determined by fluoroscopic imaging of an anaesthetized mouse. Each MOBY time frame is loaded in a dedicated treatment planning system (small animal radiotherapy-Plan) and is irradiated by a full arc with a 5-mm circular collimator. Mean and time-dependent organ doses are calculated for the tumour, heart and spinal cord. RESULTS: Depending on the location of the lung tumour, an overestimation of the mean tumour dose up to 11% is found. The mean heart dose could be both overestimated or underestimated because the heart moves in or out of the irradiation field depending on the beam target location. The respiratory motion does not affect the mean spinal cord dose. A dose gradient is visible in the time-dependent tumour dose distribution. CONCLUSION: In the future, new methods need to be developed to track the lung tumour motion before preclinical irradiation to adjust the irradiation plan. Margins, collimator diameter and target dose could be changed easily, but they all have their drawbacks. State-of-the-art clinical techniques such as respiratory gating or motion tracking may offer a solution for the cold spots in the time-dependent tumour dose. Advances in knowledge: A suitable method is found to quantify changes in organ dose due to respiratory motion in mouse lung tumour image-guided precision irradiation.
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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".