Diagnostic Accuracy of Dynamic Contrast-Enhanced MR Imaging Using a Phase-Derived Vascular Input Function in the Preoperative Grading of Gliomas
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The accuracy of tumor plasma volume and K(trans) estimates obtained with DCE MR imaging may have inaccuracies introduced by a poor estimation of the VIF. In this study, we evaluated the diagnostic accuracy of a novel technique by using a phase-derived VIF and "bookend" T1 measurements in the preoperative grading of patients with suspected gliomas. MATERIALS AND METHODS: This prospective study included 46 patients with a new pathologically confirmed diagnosis of glioma. Both magnitude and phase images were acquired during DCE MR imaging for estimates of K(trans)_φ and V(p_)φ (calculated from a phase-derived VIF and bookend T1 measurements) as well as K(trans)_SI and V(p_)SI (calculated from a magnitude-derived VIF without T1 measurements). RESULTS: Median K(trans)_φ values were 0.0041 minutes(-1) (95 CI, 0.00062-0.033), 0.031 minutes(-1) (0.011-0.150), and 0.088 minutes(-1) (0.069-0.110) for grade II, III, and IV gliomas, respectively (P ≤ .05 for each). Median V(p_)φ values were 0.64 mL/100 g (0.06-1.40), 0.98 mL/100 g (0.34-2.20), and 2.16 mL/100 g (1.8-3.1) with P = .15 between grade II and III gliomas and P = .015 between grade III and IV gliomas. In differentiating low-grade from high-grade gliomas, AUCs for K(trans)_φ, V(p_φ), K(trans)_SI, and V(p_)SI were 0.87 (0.73-1), 0.84 (0.69-0.98), 0.81 (0.59-1), and 0.84 (0.66-0.91). The differences between the AUCs were not statistically significant. CONCLUSIONS: K(trans)_φ and V(p_)φ are parameters that can help in differentiating low-grade from high-grade gliomas.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".