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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".