Initial Investigation into Microbleeds and White Matter Signal Changes following Radiotherapy for Low-Grade and Benign Brain Tumors Using Ultra-High-Field MRI Techniques
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: External beam radiation therapy is a common treatment for many brain neoplasms. While external beam radiation therapy adheres to dose limits to protect the uninvolved brain, areas of high dose to normal tissue still occur. Patients treated with chemoradiotherapy can have adverse effects such as microbleeds and radiation necrosis, but few studies exist of patients treated without chemotherapy. MATERIALS AND METHODS: Ten patients were treated for low-grade or benign neoplasms with external beam radiation therapy only and scanned within 12-36 months following treatment with a 7T MR imaging scanner. A multiecho gradient-echo sequence was acquired and postprocessed into SWI, quantitative susceptibility mapping, and apparent transverse relaxation maps. Six patients returned for follow-up imaging approximately 18 months following their first research scan and were imaged with the same techniques. RESULTS: At the first visit, 7/10 patients had microbleeds evident on SWI, quantitative susceptibility mapping, and apparent transverse relaxation. All microbleeds were within a dose region of >45 Gy. Additionally, 4/10 patients had asymptomatic WM signal changes evident on standard imaging. Further analysis with our technique revealed that these lesions were venocentric, suggestive of a neuroinflammatory process. CONCLUSIONS: There exists a potential for microbleeds in patients treated with external beam radiation therapy without chemotherapy. This finding is of clinical relevance because it could be a precursor of future neurovascular disease and indicates that additional care should be taken when using therapies such as anticoagulants. Additionally, the appearance of venocentric WM lesions could be suggestive of a neuroinflammatory mechanism that has been suggested in diseases such as MS. Both findings merit further investigation in a larger population set.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 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".