Radiologic Patterns of Necrosis After Proton Therapy of Skull Base Tumors
Bibliographic record
Abstract
BACKGROUND: Discrimination between radiation necrosis and tumor progression after radiation therapy represents a radiologic challenge. The aim of our investigation is to identify patterns of radiation necrosis on brain magnetic resonance imaging (MRI) and positron emission tomography (PET) with Fluoroethyltyrosin (FET) after proton beam therapy (PBT) for skull base tumors. MATERIAL AND METHODS: Five consecutive patients with extra-axial neoplasms were included, presenting a total of eight radiation necrosis lesions (three clival chordomas; two petroclival chondrosarcomas; two women; mean age: 49 ± 18.2 years). Radiation necrosis was defined as the appearance of abnormal enhancement on MRI after PBT decreasing over time, and additional histopathologic confirmation in one patient. MRI and PET imaging were retrospectively analyzed by two experienced radiologists in consensus. RESULTS: All lesions were localized close to the primary tumor in the field of irradiation. Three patients showed bilateral symmetrical lesions. All lesions showed T2 hyperintensity and T1 hypointensity. Cerebral blood volume (CBV) was reduced in all available studies. None of the lesions showed a restricted diffusion. FET-PET (three patients) showed a higher uptake in four out of five lesions; three of which had a mean tumor-to-background (TBRmean) uptake lower than 1.95 and FET uptake increasing over time and were correctly classified into radiation necrosis. CONCLUSIONS: Most radiation necroses were in direct continuity with the primary tumor mimicking tumor progression. The most consistent imaging findings for PBT radiation necrosis are low CBV without restricted diffusion and FET-PET TBRmean lower than 1.95 or increasing uptake over time. Bilateral symmetric involvement may be another indicator of radiation necrosis. Critères radiologiques de la nécrose après protonthérapie des tumeurs de la base du crâne.
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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 | 0.002 |
| 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".