OS3 - 187 Differentiating Radionecrosis from Tumor Progression Using IVIM perfusion Fraction in Brain Metastases Treated with Radiosurgery
Bibliographic record
Abstract
Radiation necrosis occurs in 5-25% of patients who undergo stereotactic radiosurgery (SRS) for brain metastases. Intravoxel incoherent motion (IVIM) uses MRI diffusion-weighted imaging (DWI) to assess regional perfusion. We investigated the utility of IVIM to differentiate recurrent tumor from radionecrosis after SRS. Patients who had SRS and subsequent surgical resection of what was thought to be either tumor progression or necrosis were included. ROIs were contoured on the pre-operative post-Gd T1-weighted images and transferred to DWI images using automated co-registration. The perfusion fraction (f) was calculated using asymptotic fitting and the mean f (fmean), 90th percentile for f (f90), mean ADC (ADCmean) and 10th percentile for ADC (ADC10) were calculated. Pathology reports were used to identify the predominant feature (necrosis versus tumor). Nine patients with ten lesions were included. One lesion exhibited pure necrosis while the other nine were mixed; three were predominantly (>75%) tumor, three predominantly necrosis, and three were equal parts of both. The perfusion fraction was significantly higher in cases with predominantly tumor compared to those with predominantly necrosis (fmean 0.10±0.01 vs 0.08±0.01, p=0.02 and f90 0.22±0.01 vs 0.14±0.02, p<0.001). ADC did not differentiate tumor from necrosis (ADCmean 0.97±0.23 vs 1.02±0.36, p=0.8 and ADC10 0.53±0.29 vs 0.76±0.29, p=0.33). The IVIM perfusion fraction is useful in differentiating recurrent tumor from radionecrosis in brain metastases treated with SRS. This is the first study to evaluate IVIM against the gold standard (histopathology).
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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".