Sci‐YIS Fri ‐ 08: Regional change in brain perfusion after fractionated stereotactic radiotherapy (FSRT) at 4 months and 3 years follow‐up
Bibliographic record
Abstract
Purpose: The change in hemodynamic parameters, such as mean transit time and cerebral blood volume, reflect the damage to vasculature. A relationship between the change in hemodynamic parameters and radiation dose delivered would help predict the degree and nature of damage, and would be most beneficial for patients with a long life‐expectancy who are at risk of long‐term radiation‐induced injury. Method and Materials: We applied the relative perfusion weighted MRI technique, currently used in stroke imaging, to calculate the relative regional mean transit time (rrMTT) and relative regional cerebral blood volume (rrCBV). We acquired data for one patient. We used a 3.0 T magnet at the Seaman Family MRI Centre in Calgary and a single‐shot echo‐planar imaging (EPI) sequence following the injection of a paramagnetic contrast agent (Gd‐DTPA‐Magnevist; Berlex, Wayne, NJ). These images have been processed to yield rrMTT and rrCBV. The patient had previously been treated with surgery, but had received no chemotherapy. The percentage change in rrMTT and rrCBV was correlated to the spatial distribution of radiation dose delivered using the Pinnacle® radiation treatment planning system. Results: Our preliminary results show that with a follow‐up time of 4 months and 3 years after receiving approximately 5000 cGy/25 fractions, rrMTT and rrCBV change significantly in normal tissue and tumour. The most important normal tissue changes occur in the near‐target area. Conclusion: The change in rrMTT and rrCBV indicates response to treatment. Perfusion weighted MRI can be used to assess the change in hemodynamic measures after radiotherapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".