Evaluation of the TG‐43 formalism for intraoperative radiotherapy dosimetry in glioblastoma treatment
Bibliographic record
Abstract
Abstract Background Intraoperative radiation therapy (IORT) using the INTRABEAM system has shown promise in glioblastoma treatment. However, accurate dosimetry remains challenging due to the low‐energy photons used and the heterogeneity of tissues in the brain. Current clinical practice relies on the TARGIT method, but more robust approaches, including the TG‐43 formalism and Monte Carlo (MC) simulations, warrant investigation for potential improvements in dose calculation accuracy. Purpose To evaluate the TG‐43 dosimetry formalism for IORT dose calculations in glioblastoma treatment using the INTRABEAM system, comparing it with the TARGIT method and MC simulations. Methods We analyzed the dose distributions in 20 patients from the INTRAGO trial. The TG‐43 formalism was validated against MC simulations in water () using global/local dose differences and gamma analysis (1%/1mm). Organ at risk (OAR) doses were calculated using TG‐43, TARGIT, , and MC in heterogeneous media (). Combined IORT and external beam radiotherapy (EBRT) doses were evaluated. Results TG‐43 showed good agreement with , with a 98.0% gamma pass rate. The mean global dose difference was 0.07% 0.29%, with TG‐43 slightly overestimating dose compared to . For OAR dose comparisons using TG‐43 as reference, TARGIT underestimated doses by 0.1%–1.7%, while showed larger differences near bony structures (up to 1.9% 1.4% for optic nerves). Combined IORT+EBRT analysis revealed more OAR constraint violations than identified by current clinical practice. Calculation times for TG‐43 (0.6 s on average) were significantly shorter than for MC simulations (16–18 h on a computing cluster). Conclusions The TG‐43 formalism provides a reasonable compromise between accuracy and computational efficiency for IORT dose calculations in glioblastoma treatment. It offers improved accuracy over TARGIT while being significantly faster and thus more feasible for intraoperative use than full MC simulations. Implementation of validated volumetric dose calculation methods like TG‐43 has the potential to improve the accuracy of IORT treatment planning and OAR dose assessment.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".