Comparison of quantitative parameters in cervix cancer measured by dynamic contrast–enhanced MRI and CT
Bibliographic record
Abstract
Cervical tumors of 38 cervix cancer patients were scanned by T(1)-weighted dynamic contrast enhanced (DCE) MRI and then by DCE-CT on the same day. Gadodiamide and iohexol were respectively used as the low-molecular-weight contrast agent in DCE-MRI and DCE-CT. Under an extended Tofts model, DCE-MRI data were analyzed using either individual arterial input functions estimated by a multiple reference tissue method or a population arterial input function by Parker et al., whereas DCE-CT data were analyzed using the arterial input function directly measured from the external iliac arteries. The derived quantitative parameters of cervical tumors were compared between DCE-MRI and DCE-CT. When using the individual multiple reference tissue method arterial input functions to analyze the DCE-MRI data, the correlation coefficients between DCE-MRI- and DCE-CT-derived parameters were, respectively, back-flux rate constant (r = 0.80), extravascular extracellular fractional volume (r = 0.73), contrast agent transfer rate (r = 0.62), and blood plasma volume (r = 0.32); when using the Parker population arterial input function, the correlation coefficients were back-flux rate constant (r = 0.79), extravascular extracellular fractional volume (r = 0.77), contrast agent transfer rate (r = 0.63), and blood plasma volume (r = 0.58). Tumor parametric maps derived by DCE-MRI and DCE-CT had very similar morphologies. However, the means of most derived quantitative parameters were significantly different between the two imaging methods. Close correlation of quantitative parameters derived from two independent imaging modalities suggests both are measuring similar tumor physiologic variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".