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A Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

文献类型：期刊论文

作者Yang, Li1; Gu, Dongsheng2; Wei, Jingwei2; Yang, Chun1; Rao, Shengxiang1; Wang, Wentao1; Chen, Caizhong1; Ding, Ying1; Tian, Jie2; Zeng, Mengsu1
刊名LIVER CANCER
出版日期2019-10-01
卷号8期号:5页码:373-386
关键词Microvascular invasion Hepatocellular carcinoma Gadoxetic acid Radiomics Nomogram
ISSN号2235-1795
DOI10.1159/000494099
通讯作者Tian, Jie(tian@ieee.org) ; Zeng, Mengsu(zengmengsu@outlook.com)
英文摘要Background: Radiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods: Two hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility. Results: Higher alpha-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram. Conclusions: The nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.
WOS关键词ACID-ENHANCED MR ; PRACTICE GUIDELINES ; SIGNAL-INTENSITY ; SURVIVAL ; RESECTION ; FEATURES ; BIOMARKER ; SELECTION ; OUTCOMES ; IMAGES
资助项目National Natural Science Foundation of China[81571661] ; Zhongshan Hospital Science Foundation[2018ZSQN23]
WOS研究方向Oncology ; Gastroenterology & Hepatology
语种英语
WOS记录号WOS:000491340200006
出版者KARGER
资助机构National Natural Science Foundation of China ; Zhongshan Hospital Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/26663]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie; Zeng, Mengsu
作者单位1.Fudan Univ, Zhongshan Hosp, Shanghai Inst Med Imaging, Dept Radiol, 180 Fenglin Rd, Shanghai 200032, Peoples R China
2.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Li,Gu, Dongsheng,Wei, Jingwei,et al. A Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma[J]. LIVER CANCER,2019,8(5):373-386.
APA Yang, Li.,Gu, Dongsheng.,Wei, Jingwei.,Yang, Chun.,Rao, Shengxiang.,...&Zeng, Mengsu.(2019).A Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma.LIVER CANCER,8(5),373-386.
MLA Yang, Li,et al."A Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma".LIVER CANCER 8.5(2019):373-386.

入库方式： OAI收割

来源：自动化研究所

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