Interindividual variability of arterial impulse response to intravenous injection of nonionic contrast agent (Iohexol) in DCE‐CT study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».