A comparison of dynamic contrast‐enhanced <scp>CT</scp> and <scp>MR</scp> imaging‐derived measurements in patients with cervical cancer
Bibliographic record
Abstract
This work is to compare the kinetic parameters derived from the DCE-CT and -MR data of a group of 37 patients with cervical cancer. The modified Tofts model and the reference tissue method were applied to estimate kinetic parameters. In the MR kinetic analyses using the modified Tofts model for each patient data set, both the arterial input function (AIF) measured from DCE-MR images and a population-averaged AIF from the literature were applied to the analyses, while the measured AIF was used for the CT kinetic analysis. The kinetic parameters obtained from both modalities were compared. Significant moderate correlations were found in modified Tofts parameters [volume transfer constant(K(trans) ) and rate constant (k(ep) )] between CT and MR analysis for MR with the measured AIFs (R = 0·45, P<0·01 and R = 0·40, P<0·01 in high-K(trans) region; R = 0·38, P<0·01 and R = 0·80, P<0·01 in low-K(trans) region) as well as with the population-averaged AIF (R = 0·59, P<0·01 and R = 0·62, P<0·01 in high-K(trans) region; R = 0·50, P<0·01 and R = 0·63, P<0·01 in low-K(trans) region), respectively. In addition, from the Bland-Altman plot analysis, it was found that the systematic biases (the mean difference) between the modalities were drastically reduced in magnitude by adopting the population-averaged AIF for the MR analysis instead of the measured ones (from 51·5% to 18·9% for K(trans) and from 21·7% to 4·1% for k(ep) in high-K(trans) region; from 73·0% to 29·4% for K(trans) and from 63·4% to 24·5% for k(ep) in low-K(trans) region). The preliminary results showed the feasibility in the interchangeable use of the two imaging modalities in assessing cervical cancers.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".