Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients

Fei Liu; Dan Liu; Kun Wang; Xiaohua Xie; Liya Su; Ming Kuang; Guangliang Huang; Baogang Peng; Yuqi Wang; Manxia Lin; Jie Tian; Xiaoyan Xie

doi:10.1159/000505694

Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients

Liver Cancer. 2020 Aug;9(4):397-413. doi: 10.1159/000505694. Epub 2020 Mar 31.

Authors

Fei Liu^{1

2

3}, Dan Liu¹, Kun Wang^{2

3}, Xiaohua Xie¹, Liya Su¹, Ming Kuang^{1

4}, Guangliang Huang¹, Baogang Peng⁴, Yuqi Wang^{2

3}, Manxia Lin¹, Jie Tian^{2

5}, Xiaoyan Xie¹

Affiliations

¹ Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
² CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
³ Department of the Artificial Intelligence Technology, University of Chinese Academy of Sciences, Beijing, China.
⁴ Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
⁵ Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China.

Abstract

Background: We aimed to evaluate the performance of a deep learning (DL)-based Radiomics strategy designed for analyzing contrast-enhanced ultrasound (CEUS) to not only predict the progression-free survival (PFS) of radiofrequency ablation (RFA) and surgical resection (SR) but also optimize the treatment selection between them for patients with very-early or early-stage hepatocellular carcinoma (HCC).

Methods: We retrospectively enrolled 419 patients examined by CEUS within 1 week before receiving RFA or SR (RFA: 214, SR: 205) from January 2008 to 2016. Two Radiomics signatures were constructed by the Radiomics model R-RFA and R-SR to stratify PFS of different treatment groups. Then, RFA and SR nomograms were built by incorporating Radiomics signatures and significant clinical variables to achieve individualized 2-year PFS prediction. Finally, we applied both Radiomics models and both nomograms to each enrolled patient to investigate whether there were space for treatment optimization and how much prognostic improvement could be expected.

Results: R-RFA and R-SR showed remarkable discrimination (C-index: 0.726 for RFA, 0.741 for SR). RFA and SR nomograms provided good 2-year PFS prediction accuracy and good calibrations. We identified 17.3% RFA patients and 27.3% SR patients should swap their treatment, so their average probability of 2-year PFS would increase 12 and 15%, respectively.

Conclusions: The proposed Radiomics models and nomograms achieved accurate preoperative prediction of PFS for RFA and SR, and they could facilitate the optimized treatment selection between them for patients with very-early or early-stage HCC.

Keywords: Contrast-enhanced ultrasound; Hepatocellular carcinoma; Radiofrequency ablation; Radiomics; Surgical resection.