Diagnostic Accuracy of Arterial Spin-Labeling, Dynamic Contrast-Enhanced, and DSC Perfusion Imaging in the Diagnosis of Recurrent High-Grade Gliomas: A Prospective Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: For patients with high-grade gliomas, the appearance of a new, enhancing lesion after surgery and chemoradiation represents a diagnostic dilemma. We hypothesized that MR perfusion without and with contrast can differentiate tumor recurrence from radiation necrosis. MATERIALS AND METHODS: In this prospective study, we performed 3 MR perfusion methods: arterial spin-labeling, DSC, and dynamic contrast enhancement. For each lesion, we measured CBF from arterial spin-labeling, uncorrected relative CBV, and leakage-corrected relative CBV from DSC imaging. The volume transfer constant and plasma volume were obtained from dynamic contrast-enhanced imaging without and with T1 mapping using modified Look-Locker inversion recovery (MOLLI). The diagnosis of tumor recurrence or radiation necrosis was determined by either histopathology for patients who underwent re-resection or radiologic follow-up for patients who did not have re-resection. RESULTS: There were 26 patients with 32 lesions, 19 lesions with tumor recurrence and 13 lesions with radiation necrosis. Compared with radiation necrosis, lesions with tumor recurrence had higher CBF (P = .033), leakage-corrected relative CBV (P = .048), and plasma volume using MOLLI T1 mapping (P = .012). For differentiating tumor recurrence from radiation necrosis, the areas under the curve were 0.81 for CBF, 0.80 for plasma volume using MOLLI T1 mapping, and 0.71 for leakage-corrected relative CBV. A correlation was found between CBF and leakage-corrected relative CBV (rs = 0.54), volume transfer constant, and plasma volume (0.50 < rs< 0.77) but not with uncorrected relative CBV (rs = 0.20, P = .29). CONCLUSIONS: In the differentiation of tumor recurrence from radiation necrosis in a newly enhancing lesion, the diagnostic value of arterial spin-labeling–derived CBF is similar to that of DSC and dynamic contrast-enhancement–derived blood volume.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".