Intensity modulated radiation therapy versus three dimensional conformal radiation therapy for treatment of high grade glioma: A radiobiological modeling study
Bibliographic record
Abstract
Treatment of glioblastoma results in a median survival of 12 months. Radiation dose escalation trials for high grade gliomas have resulted in modest improvements in survival in selected patients with small peripheral tumors at the expense of normal brain toxicity. Neurotoxicity includes radiation necrosis but it is increasingly recognized that long-term survivors may develop neuro-cognitive deficits. Tumor control probability (TCP) and normal tissue complication probability (NTCP) are radiobiological models used to predict treatment outcomes. This study assesses the impact of radiation dose escalation from 59.6 Gy to 90 Gy on TCP and NTCP in ten patients planned with Three Dimensional Conformal Therapy (3DCRT) and Intensity Modulated Radiation Therapy (IMRT). No difference in TCP was observed between 3DCRT and IMRT at doses of 59.4 Gy and 90 Gy. However, dose escalation to 90 Gy resulted in about 25% relative TCP increase. Compared to 3DCRT, dose escalation with IMRT significantly reduced NTCP by 70% (10.75% v. 3.75%, respectively). As a result, highly conformal techniques are recommended to obviate radiation exposure of normal brain especially when radiation dose escalation is used. Further understanding of the molecular mechanisms underlying neurotoxicity will allow the development of more precise radiobiological predictive models and of approaches to prevent or treat radiation-induced brain damage.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".