Rationale and Objectives
To construct preoperative models based on CT radiomics, radiologic and clinical features
to predict recurrence-free survival (RFS) after liver resection (LR) of BCLC 0 to
B stage hepatocellular carcinoma (HCC) and to classify the prognosis.
Materials and Methods
This study retrospectively analyzed 161 HCC patients who underwent radical LR. Two
methods, the least absolute shrinkage and selection operator and random survival forest
analysis, were performed for radiomics signature (RS) construction. Univariate and
multivariate stepwise Cox regression analyses were performed to establish a combined
nomogram (RCN) of RS and clinical parameters and a clinical nomogram (CN). The performance
of the models was assessed comprehensively using Harrell's concordance index (C-index),
the calibration curve, and decision curve analysis. The discrimination accuracy of
the models was compared using integrated discrimination improvement index (IDI). The
risk stratification effect was assessed with Kaplan–Meier survival analysis and subgroup
analysis.
Results
The RCN achieved a C-index of 0.792/0.758 in the training/validation set, which was
higher than the CN, RS, and BCLC stage system. The discriminatory accuracy of the
RCN was improved when compared to the CN, RS, and BCLC staging systems (IDI > 0).
Decision curve analysis reflected the clinical net benefit of the RCN. The RCN allows
risk stratification of patients in different clinical subgroups.
Conclusion
The integrated model combining RS and clinical factors can more effectively predict
RFS after LR of BCLC 0 to B stage HCC patients and can effectively stratify the prognostic
risk.
Key Words
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Article info
Publication history
Published online: January 16, 2023
Accepted:
December 23,
2022
Received in revised form:
December 19,
2022
Received:
October 31,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
