Author:
Chen Mingzhen,Kong Chunli,Qiao Enqi,Chen Yaning,Chen Weiyue,Jiang Xiaole,Fang Shiji,Zhang Dengke,Chen Minjiang,Chen Weiqian,Ji Jiansong
Abstract
Abstract
Objectives
This study compared the accuracy of predicting transarterial chemoembolization (TACE) outcomes for hepatocellular carcinoma (HCC) patients in the four different classifiers, and comprehensive models were constructed to improve predictive performance.
Methods
The subjects recruited for this study were HCC patients who had received TACE treatment from April 2016 to June 2021. All participants underwent enhanced MRI scans before and after intervention, and pertinent clinical information was collected. Registry data for the 144 patients were randomly assigned to training and test datasets. The robustness of the trained models was verified by another independent external validation set of 28 HCC patients. The following classifiers were employed in the radiomics experiment: machine learning classifiers k-nearest neighbor (KNN), support vector machine (SVM), the least absolute shrinkage and selection operator (Lasso), and deep learning classifier deep neural network (DNN).
Results
DNN and Lasso models were comparable in the training set, while DNN performed better in the test set and the external validation set. The CD model (Clinical & DNN merged model) achieved an AUC of 0.974 (95% CI: 0.951–0.998) in the training set, superior to other models whose AUCs varied from 0.637 to 0.943 (p < 0.05). The CD model generalized well on the test set (AUC = 0.831) and external validation set (AUC = 0.735).
Conclusions
DNN model performs better than other classifiers in predicting TACE response. Integrating with clinically significant factors, the CD model may be valuable in pre-treatment counseling of HCC patients who may benefit the most from TACE intervention.
Funder
National Key Research and Development projects intergovernmental cooperation in science and technology of China
National Natural Science Foundation of China
Natural Science Foundation of Zhejiang Province
Zhejiang Provincial Key Laboratory of Wood Science and Technology
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging
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