AUTHOR=Zhang Lei , Xia Wei , Yan Zhi-Ping , Sun Jun-Hui , Zhong Bin-Yan , Hou Zhong-Heng , Yang Min-Jie , Zhou Guan-Hui , Wang Wan-Sheng , Zhao Xing-Yu , Jian Jun-Ming , Huang Peng , Zhang Rui , Zhang Shen , Zhang Jia-Yi , Li Zhi , Zhu Xiao-Li , Gao Xin , Ni Cai-Fang TITLE=Deep Learning Predicts Overall Survival of Patients With Unresectable Hepatocellular Carcinoma Treated by Transarterial Chemoembolization Plus Sorafenib JOURNAL=Frontiers in Oncology VOLUME=10 YEAR=2020 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.593292 DOI=10.3389/fonc.2020.593292 ISSN=2234-943X ABSTRACT=Objectives

To develop and validate a deep learning-based overall survival (OS) prediction model in patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) plus sorafenib.

Methods

This retrospective multicenter study consisted of 201 patients with treatment-naïve, unresectable HCC who were treated with TACE plus sorafenib. Data from 120 patients were used as the training set for model development. A deep learning signature was constructed using the deep image features from preoperative contrast-enhanced computed tomography images. An integrated nomogram was built using Cox regression by combining the deep learning signature and clinical features. The deep learning signature and nomograms were also externally validated in an independent validation set of 81 patients. C-index was used to evaluate the performance of OS prediction.

Results

The median OS of the entire set was 19.2 months and no significant difference was found between the training and validation cohort (18.6 months vs. 19.5 months, P = 0.45). The deep learning signature achieved good prediction performance with a C-index of 0.717 in the training set and 0.714 in the validation set. The integrated nomogram showed significantly better prediction performance than the clinical nomogram in the training set (0.739 vs. 0.664, P = 0.002) and validation set (0.730 vs. 0.679, P = 0.023).

Conclusion

The deep learning signature provided significant added value to clinical features in the development of an integrated nomogram which may act as a potential tool for individual prognosis prediction and identifying HCC patients who may benefit from the combination therapy of TACE plus sorafenib.