Diagnostic Accuracy of Centrally Restricted Diffusion Sign in Cerebral Metastatic Disease: Differentiating Radiation Necrosis from Tumor Recurrence
Bibliographic record
Abstract
Purpose: The centrally restricted diffusion sign of diffusion-weighted imaging (DWI) is associated with radiation necrosis (RN) in treated gliomas. Our goal was to evaluate its diagnostic accuracy to distinguish RN from tumor recurrence (TR) in treated brain metastases. Methods: Retrospective study of consecutive patients with brain metastases who developed a newly centrally necrotic lesion after radiotherapy (RT). One reader placed regions of interest (ROI) in the enhancing solid lesion and the non-enhancing central necrosis on the apparent diffusion coefficient (ADC) map. Two readers qualitatively assessed the presence of the centrally restricted diffusion sign. The final diagnosis was made by histopathology (n = 39) or imaging follow-up (n = 2). Differences between groups were assessed by Fisher’s exact or Mann–Whitney U tests. Diagnostic accuracy and inter-reader agreement were evaluated using receiver operating characteristic (ROC) curve analysis and kappa scores. Results: Forty-one lesions (32 predominant RN; 9 predominant TR) were analyzed. An ADC value ≤ 1220 × 10-6 mm2/s (sensitivity 74%, specificity 89%, area under the curve [AUC] .85 [95% confidence interval {CI}, .70–.94] P < .0001) from the necrosis and an ADC necrosis/enhancement ratio ≤1.37 (sensitivity 74%, specificity 89%, AUC .82 [95% CI, .67–.93] P < .0001) provided the highest performance for RN diagnosis. The qualitative centrally restricted diffusion sign had a sensitivity of 69% (95% CI, .50–.83), specificity of 77% (95% CI, .40–.96), and a moderate (k = .49) inter-reader agreement for RN diagnosis. Conclusions: Radiation necrosis is associated with lower ADC values in the central necrosis than TR. A moderate interobserver agreement might limit the qualitative assessment of the centrally restricted diffusion sign.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".