Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma

Objectives The study was to develop a Gd-EOB-DTPA-enhanced MRI radiomics model for preoperative prediction of VETC and patient prognosis in hepatocellular cancer (HCC). Methods The study included 182 (training cohort: 128; validation cohort: 54) HCC patients who underwent preoperative Gd-EOB-DTPA-en...

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Bibliographic Details
Published in:European radiology 2022-02, Vol.32 (2), p.959-970
Main Authors: Yu, Yixing, Fan, Yanfen, Wang, Ximing, Zhu, Mo, Hu, Mengjie, Shi, Cen, Hu, Chunhong
Format: Article
Language:English
Subjects:
Algorithms
Cancer
Carcinoma, Hepatocellular - diagnostic imaging
Clusters
Confidence intervals
Contrast Media
Diagnostic Radiology
Encapsulation
Feature extraction
Gadolinium
Gadolinium DTPA
Hepatobiliary-Pancreas
Hepatocellular carcinoma
Humans
Imaging
Internal Medicine
Interventional Radiology
Learning algorithms
Liver cancer
Liver Neoplasms - diagnostic imaging
Machine learning
Magnetic Resonance Imaging
Medical prognosis
Medicine
Medicine & Public Health
Neuroradiology
Patients
Predictions
Prognosis
Radiology
Radiomics
Retrospective Studies
Statistical analysis
Survival
Survival analysis
Tumors
Ultrasound
Vessels
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Summary:Objectives The study was to develop a Gd-EOB-DTPA-enhanced MRI radiomics model for preoperative prediction of VETC and patient prognosis in hepatocellular cancer (HCC). Methods The study included 182 (training cohort: 128; validation cohort: 54) HCC patients who underwent preoperative Gd-EOB-DTPA-enhanced MRI. Volumes of interest including intratumoral and peritumoral regions were manually delineated in the hepatobiliary phase images, from which 1316 radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the useful features. Clinical, intratumoral, peritumoral, combined radiomics, and clinical radiomics models were established using machine learning algorithms. The Kaplan–Meier survival analysis was used to assess early recurrence and progression-free survival (PFS) in the VETC + and VETC- patients. Results In the validation cohort, the area under the curves (AUCs) of radiomics models were higher than that of the clinical model using random forest (all p   0.05). Early recurrence and PFS were significantly different between the PR-predicted VETC + and VETC- HCC patients ( p  
ISSN:0938-7994
1432-1084
DOI:10.1007/s00330-021-08250-9