©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma
Zhe Huang, Zhu Shu, Rong-Hua Zhu, Jun-Yi Xin, Ling-Ling Wu, Han-Zhang Wang, Jun Chen, Zhi-Wei Zhang, Hong-Chang Luo, Kai-Yan Li
Zhe Huang, Zhu Shu, Jun-Yi Xin, Ling-Ling Wu, Hong-Chang Luo, Kai-Yan Li, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Rong-Hua Zhu, Zhi-Wei Zhang, Institute of Hepato-pancreato-bililary Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Han-Zhang Wang, Jun Chen, PDx Advanced Applications, GE Healthcare, Shanghai 200020, China
Author contributions: Huang Z contributed to study concept and design, data acquisition, analysis, and interpretation, and manuscript drafting; Wang HZ and Chen J contributed to the implementation and analysis of all machine learning methods; Huang Z, Zhu S, Wu XB, and Zhu RH contributed to image interpretation and segmentation; Zhu S, Wu XB, Zhang ZW, and Li KY contributed to data acquisition; Huang Z, Li KY, Wang HZ, and Zhu RH contributed to study concept and design, critical revision of the manuscript for important intellectual content, and study supervision.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Approval No. TJ-IRB20190401).
Informed consent statement: The Ethics Committee of Tongji Hospital waived the requirement to obtain written informed consent of all participants.
Conflict-of-interest statement: There are no conflicts of interest to report.
Data sharing statement: All data generated or analyzed during this study are included in this article and/or its supplementary material files. Further inquiries can be directed to the corresponding author.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Corresponding author: Kai-Yan Li, MD, Director, Doctor, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 95 Jiefang Avenue, Qiaokou District, Wuhan 430030, Hubei Province, China.
liky20006@126.com
Received: September 15, 2022
Peer-review started: September 15, 2022
First decision: October 19, 2022
Revised: October 21, 2022
Accepted: November 21, 2022
Article in press: November 21, 2022
Published online: December 15, 2022
Processing time: 87 Days and 15.1 Hours
BACKGROUND
Hepatocellular carcinoma (HCC) is the most common primary liver malignancy.
AIM
To predict early recurrence (ER) and overall survival (OS) in patients with HCC after radical resection using deep learning-based radiomics (DLR).
METHODS
A total of 414 consecutive patients with HCC who underwent surgical resection with available preoperative grayscale and contrast-enhanced ultrasound images were enrolled. The clinical, DLR, and clinical + DLR models were then designed to predict ER and OS.
RESULTS
The DLR model for predicting ER showed satisfactory clinical benefits [area under the curve (AUC)] = 0.819 and 0.568 in the training and testing cohorts, respectively), similar to the clinical model (AUC = 0.580 and 0.520 in the training and testing cohorts, respectively; P > 0.05). The C-index of the clinical + DLR model in the prediction of OS in the training and testing cohorts was 0.800 and 0.759, respectively. The clinical + DLR model and the DLR model outperformed the clinical model in the training and testing cohorts (P < 0.001 for all). We divided patients into four categories by dichotomizing predicted ER and OS. For patients in class 1 (high ER rate and low risk of OS), retreatment (microwave ablation) after recurrence was associated with improved survival (hazard ratio = 7.895, P = 0.005).
CONCLUSION
Compared to the clinical model, the clinical + DLR model significantly improves the accuracy of predicting OS in HCC patients after radical resection.
Core Tip: Multivariate Cox regression analysis confirmed that age [hazard ratio (HR) = 1.01], carbohydrate antigen 19-9 (HR = 0.60), tumor size (HR = 1.11), echogenicity (HR = 0.82), and deep learning-based radiomics (DLR, HR = 4.33) were independent predictors of survival outcome (P < 0.05 for all). The concordance index of the clinical + DLR model in the training and testing cohorts was 0.800 and 0.759, respectively. We divided patients into four categories by dichotomizing predicted early recurrence and survival outcome. We found that for patients with class 1 (high early recurrence rate and low risk of survival outcome), retreatment after recurrence was associated with improved survival.
