Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection

Abstract

Background and aims: Current prediction models for early recurrence of hepatocellular carcinoma (HCC) after surgical resection remain unsatisfactory. The aim of this study was to develop evolutionary learning-derived prediction models with interpretability using both clinical and radiomic features to predict early recurrence of HCC after surgical resection.

Methods: Consecutive 517 HCC patients receiving surgical resection with available contrast-enhanced computed tomography (CECT) images before resection were retrospectively enrolled. Patients were randomly assigned to a training set (n = 362) and a test set (n = 155) in a ratio of 7:3. Tumor segmentation of all CECT images including noncontrast phase, arterial phase, and portal venous phase was manually performed for radiomic feature extraction. A novel evolutionary learning-derived method called genetic algorithm for predicting recurrence after surgery of liver cancer (GARSL) was proposed to design prediction models for early recurrence of HCC within 2 years after surgery.

Results: A total of 143 features, including 26 preoperative clinical features, 5 postoperative pathological features, and 112 radiomic features were used to develop GARSL preoperative and postoperative models. The area under the receiver operating characteristic curves (AUCs) for early recurrence of HCC within 2 years were 0.781 and 0.767, respectively, in the training set, and 0.739 and 0.741, respectively, in the test set. The accuracy of GARSL models derived from the evolutionary learning method was significantly better than models derived from other well-known machine learning methods or the early recurrence after surgery for liver tumor (ERASL) preoperative (AUC = 0.687, p < 0.001 vs. GARSL preoperative) and ERASL postoperative (AUC = 0.688, p < 0.001 vs. GARSL postoperative) models using clinical features only.

Conclusion: The GARSL models using both clinical and radiomic features significantly improved the accuracy to predict early recurrence of HCC after surgical resection, which was significantly better than other well-known machine learning-derived models and currently available clinical models.

Keywords: Evolutionary learning; Hepatocellular carcinoma; Machine learning; Recurrence; Surgery.

Conflict of interest statement

Y.-H.H. has received research grants from Gilead Sciences and Bristol-Meyers Squibb, and honoraria from Abbvie, Gilead Sciences, Bristol-Meyers Squibb, Ono Pharmaceutical, Merck Sharp & Dohme, Eisai, Eli Lilly, Ipsen, and Roche and has served in an advisory role for Abbvie, Gilead Sciences, Bristol-Meyers Squibb, Ono Pharmaceuticals, Eisai, Eli Lilly, Ipsen, Merck Sharp & Dohme, and Roche. The other authors declare no conflicts of interest.

Copyright © 2021 by The Author(s). Published by S. Karger AG, Basel.

Figures

Fig. 1

Illustrated flowchart of developing the evolutionary learning-derived method GARSL. GARSL, genetic algorithm for predicting recurrence after surgery of liver cancer.

Fig. 2

Receiver operating characteristic curves of the GARSL and ERASL models in predicting early recurrence in the test set. AUC, area under the receiver operating characteristic curve; ERASL, early recurrence after surgery for liver tumor; GARSL, genetic algorithm for predicting recurrence after surgery of liver cancer.

Fig. 3

Kaplan-Meier curves of the early RFS stratified by the GARSL and ERASL models. GARSL preoperative model (a), GARSL postoperative model (b), ERASL-pre model (c), ERASL-post model (d). ERASL, early recurrence after surgery for liver tumor; RFS, recurrence-free survival; GARSL, genetic algorithm for predicting recurrence after surgery of liver cancer.