Development and validation of a deep learning model for survival prognosis of transcatheter arterial chemoembolization in patients with intermediate-stage hepatocellular carcinoma

Hairui Wang; Yuchan Liu; Nan Xu; Yuanyuan Sun; Shihan Fu; Yunuo Wu; Chunhe Liu; Lei Cui; Zhaoyu Liu; Zhihui Chang; Shu Li; Kexue Deng; Jiangdian Song

doi:10.1016/j.ejrad.2022.110527

Development and validation of a deep learning model for survival prognosis of transcatheter arterial chemoembolization in patients with intermediate-stage hepatocellular carcinoma

Eur J Radiol. 2022 Nov:156:110527. doi: 10.1016/j.ejrad.2022.110527. Epub 2022 Sep 19.

Authors

Hairui Wang¹, Yuchan Liu², Nan Xu³, Yuanyuan Sun⁴, Shihan Fu⁵, Yunuo Wu⁶, Chunhe Liu³, Lei Cui³, Zhaoyu Liu¹, Zhihui Chang¹, Shu Li³, Kexue Deng², Jiangdian Song⁷

Affiliations

¹ Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China.
² Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, Anhui 230036, China.
³ School of Health Management, China Medical University, Shenyang, Liaoning 110122, China.
⁴ Department of Genetics, College of Life Sciences, China Medical University, Shenyang, Liaoning 110122, China.
⁵ International School, Beijing University of Posts and Telecommunications, Beijing 100876, China.
⁶ School of Life Sciences, China Medical University, Shenyang, Liaoning 110122, China.
⁷ School of Health Management, China Medical University, Shenyang, Liaoning 110122, China. Electronic address: song.jd0910@gmail.com.

PMID: 36152524
DOI: 10.1016/j.ejrad.2022.110527

Abstract

Purpose: We aimed to develop a deep learning-based approach to evaluate both time-to-progression (TTP) and overall survival (OS) prognosis of transcatheter arterial chemoembolization (TACE) in treatment-naïve patients with intermediate-stage hepatocellular carcinoma (HCC) and compare the approach's performance with those of radiomics and clinical models.

Methods: EfficientNetV2 was used to build a prognosis model for treatment-naïve patients with HCC. Data of 414 intermediate-stage HCC patients from one participant center were collected to construct the training and validation datasets (70%:30%) for TTP prognosis, while data of 129 intermediate-stage HCC patients from another participant center were collected as the test dataset for both TTP and OS prognosis. Three radiomics and three clinical models were then constructed for comparison.

Results: Patients with EfficientNetV2-based model score ≤ 0.5 had better TTP than those with higher scores (hazard ratio [HR]: 0.32, 95%CI: 0.22-0.46, P < 0.0001; HR: 0.28, 95%CI: 0.20-0.41, P < 0.0001; and HR: 0.55, 95%CI: 0.36-0.88, P = 0.005 in the training, validation, and test datasets, respectively). Patients with model score ≤ 0.5 had better OS (38.8 months vs 20.9 months, HR: 0.58, 95%CI: 0.37-0.90, P = 0.008). Compared with the radiomics (intra-tumoral and peri-tumoral) and three clinical models, the EfficientNetV2-based model showed better survival prognosis for TACE (P < 0.05) in the test dataset.

Conclusions: The EfficientNetV2-based model enables assessment of both TTP and OS prognosis of TACE in treatment-naïve, intermediate-stage HCC. Patients with lower scores will benefit from TACE. The model can potentially be used by clinicians to improve decision making regarding TACE treatment choices.

Keywords: Computed tomography; Convolutional neural network; Hepatocellular carcinoma; Machine learning; Prognosis.