Prospective evaluation of Gadoxetate-enhanced magnetic resonance imaging and computed tomography for hepatocellular carcinoma detection and transplant eligibility assessment with explant histopathology correlation
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
BACKGROUND: We aimed to prospectively compare the diagnostic performance of gadoxetic acid-enhanced MRI (EOB-MRI) and contrast-enhanced Computed Tomography (CECT) for hepatocellular carcinoma (HCC) detection and liver transplant (LT) eligibility assessment in cirrhotic patients with explant histopathology correlation. METHODS: In this prospective, single-institution ethics-approved study, 101 cirrhotic patients were enrolled consecutively from the pre-LT clinic with written informed consent. Patients underwent CECT and EOB-MRI alternately every 3 months until LT or study exclusion. Two blinded radiologists independently scored hepatic lesions on CECT and EOB-MRI utilizing the liver imaging reporting and data system (LI-RADS) version 2018. Liver explant histopathology was the reference standard. Pre-LT eligibility accuracies with EOB-MRI and CECT as per Milan criteria (MC) were assessed in reference to post-LT explant histopathology. Lesion-level and patient-level statistical analyses were performed. RESULTS: Sixty patients (49 men; age 33-72 years) underwent LT successfully. One hundred four non-treated HCC and 42 viable HCC in previously treated HCC were identified at explant histopathology. For LR-4/5 category lesions, EOB-MRI had a higher pooled sensitivity (86.7% versus 75.3%, p < 0.001) but lower specificity (84.6% versus 100%, p < 0.001) compared to CECT. EOB-MRI had a sensitivity twice that of CECT (65.9% versus 32.2%, p < 0.001) when all HCC identified at explant histopathology were included in the analysis instead of imaging visible lesions only. Disregarding the hepatobiliary phase resulted in a significant drop in EOB-MRI performance (86.7 to 72.8%, p < 0.001). EOB-MRI had significantly lower pooled sensitivity and specificity versus CECT in the LR5 category with lesion size < 2 cm (50% versus 79%, p = 0.002 and 88.9% versus 100%, p = 0.002). EOB-MRI had higher sensitivity (84.8% versus 75%, p < 0.037) compared to CECT for detecting < 2 cm viable HCC in treated lesions. Accuracies of LT eligibility assessment were comparable between EOB-MRI (90-91.7%, p = 0.156) and CECT (90-95%, p = 0.158). CONCLUSION: EOB-MRI had superior sensitivity for HCC detection; however, with lower specificity compared to CECT in LR4/5 category lesions while it was inferior to CECT in the LR5 category under 2 cm. The accuracy for LT eligibility assessment based on MC was not significantly different between EOB-MRI and CECT. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03342677 , Registered: November 17, 2017.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 découle