Back to Search Start Over

Bi-regional dynamic contrast-enhanced MRI for prediction of microvascular invasion in solitary BCLC stage A hepatocellular carcinoma

Authors :
Yongjian Zhu
Bing Feng
Peng Wang
Bingzhi Wang
Wei Cai
Shuang Wang
Xuan Meng
Sicong Wang
Xinming Zhao
Xiaohong Ma
Source :
Insights into Imaging, Vol 15, Iss 1, Pp 1-17 (2024)
Publication Year :
2024
Publisher :
SpringerOpen, 2024.

Abstract

Abstract Objectives To construct a combined model based on bi-regional quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), as well as clinical-radiological (CR) features for predicting microvascular invasion (MVI) in solitary Barcelona Clinic Liver Cancer (BCLC) stage A hepatocellular carcinoma (HCC), and to assess its ability for stratifying the risk of recurrence after hepatectomy. Methods Patients with solitary BCLC stage A HCC were prospective collected and randomly divided into training and validation sets. DCE perfusion parameters were obtained both in intra-tumoral region (ITR) and peritumoral region (PTR). Combined DCE perfusion parameters (C DCE) were constructed to predict MVI. The combined model incorporating C DCE and CR features was developed and evaluated. Kaplan–Meier method was used to investigate the prognostic significance of the model and the survival benefits of different hepatectomy approaches. Results A total of 133 patients were included. Total blood flow in ITR and arterial fraction in PTR exhibited the best predictive performance for MVI with areas under the curve (AUCs) of 0.790 and 0.792, respectively. C DCE achieved AUCs of 0.868 (training set) and 0.857 (validation set). A combined model integrated with the α-fetoprotein, corona enhancement, two-trait predictor of venous invasion, and C DCE could improve the discrimination ability to AUCs of 0.966 (training set) and 0.937 (validation set). The combined model could stratify the prognosis of HCC patients. Anatomical resection was associated with a better prognosis in the high-risk group (p

Details

Language :
English
ISSN :
18694101
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Insights into Imaging
Publication Type :
Academic Journal
Accession number :
edsdoj.8945b1ba3fde48dc9e4c6726c09f9dd8
Document Type :
article
Full Text :
https://doi.org/10.1186/s13244-024-01720-w