Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients | |
Liu, Fei1,2,3; Liu, Dan1; Wang, Kun2,3; Xie, Xiaohua1; Su, Liya1; Kuang, Ming1,4; Huang, Guangliang1; Peng, Baogang4; Wang, Yuqi2,3; Lin, Manxia1; Tian, Jie2,5; Xie, Xiaoyan1 | |
发表期刊 | LIVER CANCER |
ISSN | 2235-1795 |
2020-08-01 | |
卷号 | 9期号:4页码:397-413 |
摘要 | 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. |
关键词 | Contrast-enhanced ultrasound Hepatocellular carcinoma Radiomics Radiofrequency ablation Surgical resection |
DOI | 10.1159/000505694 |
关键词[WOS] | RADIOFREQUENCY ABLATION ; SURGICAL RESECTION ; HEPATIC RESECTION ; RECURRENCE ; TRIAL ; CLASSIFICATION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Ministry of Science and Technology of China[2017YFA0205200] ; Ministry of Science and Technology of China[2016YFC0103803] ; State Key Project on Infectious Diseases of China[2018ZX10723204] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81530055] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[KFJ-STSZDTP-059] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[XDBS01030200] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Science and Technology Development Special Fund of Guangdong Province[2017A020215011] |
项目资助者 | Ministry of Science and Technology of China ; State Key Project on Infectious Diseases of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences ; Science and Technology Development Special Fund of Guangdong Province |
WOS研究方向 | Oncology ; Gastroenterology & Hepatology |
WOS类目 | Oncology ; Gastroenterology & Hepatology |
WOS记录号 | WOS:000556410900004 |
出版者 | KARGER |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40347 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Lin, Manxia; Tian, Jie; Xie, Xiaoyan |
作者单位 | 1.Sun Yat Sen Univ, Inst Diagnost & Intervent Ultrasound, Dept Med Ultrason, Affiliated Hosp 1, 58 Zhongshan Second Rd, Guangzhou 510080, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100191, Peoples R China 3.Univ Chinese Acad Sci, Dept Artificial Intelligence Technol, Beijing, Peoples R China 4.Sun Yat Sen Univ, Dept Liver Surg, Affiliated Hosp 1, Guangzhou, Peoples R China 5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liu, Fei,Liu, Dan,Wang, Kun,et al. Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients[J]. LIVER CANCER,2020,9(4):397-413. |
APA | Liu, Fei.,Liu, Dan.,Wang, Kun.,Xie, Xiaohua.,Su, Liya.,...&Xie, Xiaoyan.(2020).Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients.LIVER CANCER,9(4),397-413. |
MLA | Liu, Fei,et al."Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients".LIVER CANCER 9.4(2020):397-413. |
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