Dosimetry Calculation in Human Glioblastoma for Radiotherapy: a Graphical User Interface with Monte Carlo Simulations
Bibliographic record
Abstract
We developed a graphical user interface based on GEANT4 and GATE to calculate particle interactions and absorbed dose estimates in phantoms and small animals such as mice. In the present work, we extended the interface to absorbed dose calculation and particle interactions in humans based on 3D CT images and radiation beam definition in a clinical setting. The images of the subject and of the beams were supplied in DICOM format. All the parameters needed to calculate the absorbed dose were obtained from these DICOM images. In this work, images of a human brain with glioblastoma were used together with the parameters of five photon beams defined in the clinic. The DICOM file of the beams contained several parameter values such as beam energy, in this case 6 MV, the dose to be deposited in the tumor as a total of 60 Gy, and the 5 beams orientation. Based on the 3D CT images of the patient brain, the whole patient head was rebuilt from the voxel intensity and size providing the real dimensions and calculated density of the head. The results show how the primary photons and secondary particles interact in the brain, and a 3D dose grid similar in dimensions and spatial resolution to the supplied images of the brain was obtained representing the absorbed dose in Gy. In conclusion, with this new interface, it is simple to enter geometrical objects, animal or human 3D images, to select the appropriate tasks from menus, and to run the simulation without the need to be familiar with computer programming or investigating the many classes of GEANT4 or GATE. The interface can also be used to simulate any type of radiation beams provided by the DICOM-RT set of the subjects.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".