Interindividual variability of arterial impulse response to intravenous injection of nonionic contrast agent (Iohexol) in DCE‐CT study
Bibliographic record
Abstract
PURPOSE: It is known that the arterial input function (AIF) in dynamic contrast-enhanced (DCE)-CT differs among patients even for fixed contrast injection protocols. Therefore, a study has been performed to investigate the interindividual variability of the AIF with respect to patient factors (such as weight, height, and age). In addition, it has been demonstrated that the relations from the interindividual variability investigation can be further used for the estimation of AIF for a patient without the requirement of measurement. METHODS: DCE-CT data for a cohort of 34 patients with cervical carcinoma were used for the investigation of interindividual variability of the AIF. To dissociate the effect of different durations of contrast injection, the arterial impulse response (AIR) to intravenous contrast injection was calculated and examined for its correlations with these patient factors. An empirical functional form was proposed to model the AIR with temporal intensity of a first pass of contrast agent followed by recirculation and quasiequilibrium state of contrast concentration. Specific features (onset time, peak time, and amplitudes) of the AIR were tested for correlations with the patient factors. Linear regression was applied to cases that show significant strong correlation between the AIR amplitudes and patient factors. The results were then used to predict the AIR for any given patient based on the patient factors. It was shown that using the predicted AIR, the AIF of the patient can be estimated without the requirement of measurement given the injection protocol is known. The method of AIF estimation was tested in DCE-CT data from another group of 14 patients. The efficacy of individually estimated AIF on pharmacokinetic analysis was assessed against the use of measured AIF and population-averaged AIF as the latter is another possible strategy for AIF generation if AIF measurement is not available. RESULTS: It was found that the amplitudes of AIR postonset time were significantly correlated with patient bodyweight at most time points (r<-0.44, P<0.01) except 8-17 s (P>0.01). When the adiabatic tissue homogeneity (ATH) and the Kety models were applied to the pharmacokinetic analysis, the mean percentage errors in kinetic parameter estimates induced by using the estimated AIF instead of the measured one for both models ranged from -4.98% to 11.19%. CONCLUSIONS: It was found that there are strong linear relationships between AIR and patient weight; thereby the AIR of a patient can be estimated with patient weight. The proposed method of AIF estimation is potentially applicable in cases where AIF measurement is not possible.
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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 | 0.005 |
| 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".