FIRST-PASS PERFUSION COMPUTED TOMOGRAPHY
Bibliographic record
Abstract
OBJECTIVE: To differentiate recurrent tumors from radiation effects and necrosis in patients with irradiated brain tumors using perfusion computed tomographic (PCT) imaging. METHODS: Twenty-two patients with previously treated brain tumors who showed recurrent or progressive enhancing lesions on follow-up magnetic resonance imaging scans and had a histopathological diagnosis underwent first-pass PCT imaging (26 PCT imaging examinations). Another eight patients with treatment-naïve, high-grade tumors (control group) also underwent PCT assessment. Perfusion maps of cerebral blood volume, cerebral blood flow, and mean transit time were generated at an Advantage Windows workstation using the CT perfusion 3.0 software (General Electric Medical Systems, Milwaukee, WI). Normalized ratios (normalized to normal white matter) of these perfusion parameters (normalized cerebral blood volume [nCBV], normalized cerebral blood flow [nCBF], and normalized mean transit time [nMTT]) were used for final analysis. RESULTS: Fourteen patients were diagnosed with recurrent tumor, and eight patients had radiation necrosis. There was a statistically significant difference between the two groups, with the recurrent tumor group showing higher mean nCBV (2.65 versus 1.10) and nCBF (2.73 versus 1.08) and shorter nMTT (0.71 versus 1.58) compared with the radiation necrosis group. For nCBV, a cutoff point of 1.65 was found to have a sensitivity of 83.3% and a specificity of 100% to diagnose recurrent tumor and radiation necrosis. Similar sensitivity and specificity were 94.4 and 87.5%, respectively, for nCBF with a cutoff point of 1.28 and 94.4 and 75%, respectively, for nMTT with a cutoff point of 1.44 to diagnose recurrent tumor and radiation necrosis. CONCLUSION: PCT may aid in differentiating recurrent tumors from radiation necrosis on the basis of various perfusion parameters. Recurrent tumors show higher nCBV and nCBF and lower nMTT compared with radiation necrosis.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".