Quality‐based pharmacokinetic model selection on DCE‐MRI for characterizing orbital lesions
Bibliographic record
Abstract
Background Although several studies have evaluated dynamic contrast‐enhanced (DCE) MRI in the orbit, showing its utility when detecting and diagnosing orbital lesions, none have evaluated the pharmacokinetic models. Purpose To provide a quality‐based pharmacokinetic model selection for characterizing orbital lesions using DCE‐MRI at 3.0T. Study Type Prospective. Population From December 2015 to April 2017, 151 patients with an orbital lesion underwent MRI prior to surgery, including a high temporal resolution DCE sequence, divided into one training and one test dataset with 100 and 51 patients, respectively. Field Strength/Sequence 3T/DCE. Assessment Six different pharmacokinetic models were tested. Statistical Tests Univariate and multivariate analyses were performed using Wilcoxon‐2‐sample tests and a logistic regression to compare parameters between malignant and benign tumors for each pharmacokinetic model for the whole cohort. Receiver operating characteristic (ROC) curve analyses were performed on the training dataset to determine area under curve (AUC) and optimal cutoff values for each pharmacokinetic model, then validated on the test dataset to calculate sensitivity, specificity, and accuracy. Results Regardless of the model, tissue blood flow and tissue blood volume values were significantly higher in malignant vs. benign lesions: 103.8–195.1 vs. 65–113.8, P [<10‐4–2.10‐4] and 21.3–36.9 vs. 15.6–33.6, P [<10‐4–0.03] respectively. Extracellular volume fraction and permeability–surface area product or transfer constant appeared to be less relevant: 17.3–27.5 vs. 22.8–28.2, P [0.01–0.7], 1.7–4.9, P [0.2–0.9] and 9.5–38.8 vs. 8.1–22.8, P [<10‐4–0.6], respectively. ROC curves showed no significant differences in AUC between the different models. The two‐compartment exchange (2CX) model ranked first for quality. Data Conclusion DCE MRI pharmacokinetic model‐derived parameters appeared to be useful for discriminating benign from malignant orbital lesions. The 2CX model provided the best quality of modeling and should be recommended. Perfusion‐related DCE parameters appeared to be significantly more relevant to the diagnostic process. Level of Evidence 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1514–1525.
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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 | 0.006 | 0.014 |
| 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".