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European Radiology
Published in: European Radiology 1/2020

Open Access 01-01-2020 | Computed Tomography | Computed Tomography

Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging

Authors: Jie Peng, Shuai Kang, Zhengyuan Ning, Hangxia Deng, Jingxian Shen, Yikai Xu, Jing Zhang, Wei Zhao, Xinling Li, Wuxing Gong, Jinhua Huang, Li Liu

Published in: European Radiology | Issue 1/2020

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Abstract

Background

We attempted to train and validate a model of deep learning for the preoperative prediction of the response of patients with intermediate-stage hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE).

Method

All computed tomography (CT) images were acquired for 562 patients from the Nan Fang Hospital (NFH), 89 patients from Zhu Hai Hospital Affiliated with Jinan University (ZHHAJU), and 138 patients from the Sun Yat-sen University Cancer Center (SYUCC). We built a predictive model from the outputs using the transfer learning techniques of a residual convolutional neural network (ResNet50). The prediction accuracy for each patch was revaluated in two independent validation cohorts.

Results

In the training set (NFH), the deep learning model had an accuracy of 84.3% and areas under curves (AUCs) of 0.97, 0.96, 0.95, and 0.96 for complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD), respectively. In the other two validation sets (ZHHAJU and SYUCC), the deep learning model had accuracies of 85.1% and 82.8% for CR, PR, SD, and PD. The ResNet50 model also had high AUCs for predicting the objective response of TACE therapy in patches and patients of three cohorts. Decision curve analysis (DCA) showed that the ResNet50 model had a high net benefit in the two validation cohorts.

Conclusion

The deep learning model presented a good performance for predicting the response of TACE therapy and could help clinicians in better screening patients with HCC who can benefit from the interventional treatment.

Key Points

• Therapy response of TACE can be predicted by a deep learning model based on CT images.
• The probability value from a trained or validation deep learning model showed significant correlation with different therapy responses.
• Further improvement is necessary before clinical utilization.
Appendix
Available only for authorised users
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Metadata
Title
Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging
Authors
Jie Peng
Shuai Kang
Zhengyuan Ning
Hangxia Deng
Jingxian Shen
Yikai Xu
Jing Zhang
Wei Zhao
Xinling Li
Wuxing Gong
Jinhua Huang
Li Liu
Publication date
01-01-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 1/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-019-06318-1

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