Improved correlation to quantitative DCE‐MRI pharmacokinetic parameters using a modified initial area under the uptake curve (mIAUC) approach
Bibliographic record
Abstract
PURPOSE: To propose a modified initial area under the uptake curve (mIAUC) dynamic contrast-enhanced (DCE)-MRI approach to achieve better distinction of underlying physiology. MATERIALS AND METHODS: The mIAUC is formulated on common characteristics of tissue contrast uptake curves observed over a wide range of physiological conditions. The new metrics, IAUC(Ktrans) and IAUC(ve), are related to the transfer constant (K(trans)) and interstitial volume (v(e)), respectively. Tissue uptake curves were simulated over a range of physiological values and analyzed using the proposed mIAUC, conventional IAUC, and Tofts' pharmacokinetic model. RESULTS: IAUC(Ktrans) and IAUC(ve) are highly correlated to the true K(trans) and v(e) (rho = 0.97 and 0.95, respectively), approaching the performance of Tofts' model based on a 1.5-s sampled arterial input function (AIF) (rho = 0.98 and 0.98) under noise conditions typical in DCE-MRI experiments. Lower correlations were obtained with conventional IAUC(60) and IAUC(120) (rho = 0.82 and 0.61) and Tofts' parameters fitted using a biexponential AIF (rho = 0.81 and 0.90). CONCLUSION: The proposed mIAUC approach retains advantages associated with nonmodel based methods (robust to noise and model fit failure, obviates need for an AIF) while providing better distinction of underlying physiological parameters. It can be a valuable alternative to pharmacokinetic modelling in the analysis of DCE-MRI data.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".