Magnetic Resonance Imaging Predictors of Hepatocellular Carcinoma Progression and Dropout in Patients in Liver Transplantation Waiting List.
Journal
Transplantation direct
ISSN: 2373-8731
Titre abrégé: Transplant Direct
Pays: United States
ID NLM: 101651609
Informations de publication
Date de publication:
Nov 2022
Nov 2022
Historique:
received:
15
03
2022
revised:
11
07
2022
accepted:
13
07
2022
entrez:
26
10
2022
pubmed:
27
10
2022
medline:
27
10
2022
Statut:
epublish
Résumé
With the rising incidence of hepatocellular carcinoma (HCC), more patients are now eligible for liver transplantation. Consequently, HCC progression and dropout from the waiting list are also anticipated to rise. We developed a predictive model based on radiographic features and alpha-fetoprotein to identify high-risk patients. This is a case-cohort retrospective study of 76 patients with HCC who were listed for liver transplantation with subsequent liver transplantation or delisting due to HCC progression. We analyzed imaging-based predictive variables including tumor margin (well- versus ill-defined), capsule bulging lesions, volumetric analysis and distance to portal vein, tumor numbers, and tumor diameter. Volumetric analysis of the index lesions was used to quantify index tumor total volume and volumetric enhancement, whereas logistic regression and receiver operating characteristic curve (ROC) analyses were used to predict the main outcome of disease progression. In univariate analyses, the following baseline variables were significantly associated with disease progression: size and number of lesions, sum of lesion diameters, lesions bulging the capsule, and total and venous-enhancing (viable) tumor volumes. Based on multivariable analyses, a risk model including lesion numbers and diameter, capsule bulging, tumor margin (infiltrative versus well-defined), and alpha-fetoprotein was developed to predict HCC progression and dropout. The model has an area under the ROC of 82%, which was significantly higher than Milan criteria that has an area under the ROC of 67%. Our model has a high predictive test for patient dropout due to HCC progression. This model can identify high-risk patients who may benefit from more aggressive HCC treatment early after diagnosis to prevent dropout due to such disease progression.
Identifiants
pubmed: 36284930
doi: 10.1097/TXD.0000000000001365
pmc: PMC9584197
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e1365Informations de copyright
Copyright © 2022 The Author(s). Transplantation Direct. Published by Wolters Kluwer Health, Inc.
Déclaration de conflit d'intérêts
A.K.K. is a consultant to Astrazeneca and Exelixis. The other authors declare no conflicts of interest.
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