A Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
Yang, Li1; Gu, Dongsheng2; Wei, Jingwei2; Yang, Chun1; Rao, Shengxiang1; Wang, Wentao1; Chen, Caizhong1; Ding, Ying1; Tian, Jie2; Zeng, Mengsu1
Source PublicationLIVER CANCER
Corresponding AuthorTian, Jie( ; Zeng, Mengsu(
AbstractBackground: 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.
KeywordMicrovascular invasion Hepatocellular carcinoma Gadoxetic acid Radiomics Nomogram
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[81571661] ; Zhongshan Hospital Science Foundation[2018ZSQN23]
Funding OrganizationNational Natural Science Foundation of China ; Zhongshan Hospital Science Foundation
WOS Research AreaOncology ; Gastroenterology & Hepatology
WOS SubjectOncology ; Gastroenterology & Hepatology
WOS IDWOS:000491340200006
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Document Type期刊论文
Corresponding AuthorTian, Jie; Zeng, Mengsu
Affiliation1.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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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