Correlation of CT and MR Perfusion and Permeability Parameters for Intracranial Tumors
Bibliographic record
Abstract
Background: Perfusion imaging, mainly MR perfusion (MRP), is performed frequently for brain tumor imaging. CT perfusion (CTP) is less studied as a method for characterizing brain tumors. The purpose of this study was to demonstrate the usefulness of CTP perfusion parameters in evaluating brain tumors and to compare it with MRP measures in the same patient population. Methods: Patients underwent CTP and MRP imaging prospectively. Images were processed with vendor-provided and vendor-neutral software. Four regions of interests were placed in whole tumor, solid portion of the tumor, region of maximum perfusion and contralateral normal white matter. Absolute and normalized values of cerebral blood flow (CBF), cerebral blood volume (CBV), and permeability were obtained for both CTP and MRP and compared using correlation and linear regression. Results: We compared CTP and MRP in 20 patients with intracranial tumors. With vendor-provided software, we found significant correlation for absolute CBV in the region of maximum perfusion (r2 = 0.26, p = 0.031) and for normalized CBV (r2 = 0.29, p = 0.020) and normalized CBF (r2 = 0.34, p = 0.011) in the whole tumor. With vendor-neutral software, we found significant correlation for normalized CBF in whole tumor (r2 = 0.38, p = 0.008) and in solid component (r2 = 0.47, p = 0.002). There were no significant correlations for the permeability parameters.Conclusions: In comparing CTP and MRP methods, several statistically significant positive correlations were seen for CBF and CBV values. CTP may potentially be used interchangeably with MRP, for imaging of brain tumors, especially when MRP is contraindicated.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".