Prediction of radiation necrosis in a rodent model using magnetic resonance imaging apparent transverse relaxation ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mstyle> <mml:msubsup> <mml:mi>R</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>∗</mml:mo> </mml:mrow> </mml:msubsup> </mml:mstyle> </mml:math> )
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Radiation necrosis remains an irreversible long-term side-effect following radiotherapy to the brain. The ability to predict areas that could ultimately develop into necrosis could lead to prevention and management of radiation necrosis. MATERIALS AND METHODS: Fischer 344 rats were irradiated using two platforms (micro-CT irradiator and x-Rad 225 IGRT) with radiation up to 30 Gy for the micro-CT and 40 Gy for the xRAD-224 to half the brain. Animals were subsequently imaged using a 9.4 T MRI scanner every 2-4 weeks for up to 28 weeks using a 7-echo gradient echo sequence. The apparent transverse relaxation constant ([Formula: see text]) was calculated and retrospectively analyzed. RESULTS: Animals irradiated with the low-dose rate micro-CT did not exhibit any symptoms or imaging changes associated with RN. Animals irradiated with the xRAD-225 exhibited imaging changes consistent with RN at week 24. Analysis of the [Formula: see text] coefficient within the lesion and hippocampus shows the potential for detection of RN up to 10 weeks prior to morphological changes. CONCLUSIONS: The ability to predict areas of RN and increases of [Formula: see text] within the hippocampus provides a method for long-term monitoring and prediction of RN.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".