Mingliang Wang

Dr. Mingliang Wang


Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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4 February 2014 in Particle & Particle Systems Characterization
Verified
Journal Article
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CZChaohui Zhou
Chaohui Zhou
HWHui Wu
Hui Wu
CHChusen Huang
Chusen Huang
MWMingliang Wang
Mingliang Wang
NJNengqin Jia
Nengqin Jia

1 September 2017 in Materials Science and Engineering: C
Verified
Journal Article
Closed access
CZChaohui Zhou
Chaohui Zhou
HWHui Wu
Hui Wu
MWMingliang Wang
Mingliang Wang
CHChusen Huang
Chusen Huang
DYDa-Peng Yang
Da-Peng Yang
NJNengqin Jia
Nengqin Jia

13 October 2021 in Frontiers in Oncology
Verified
Journal Article
Open access
XZXiangtian Zhao
Xiangtian Zhao
YZYukun Zhou
Yukun Zhou
YZYuan Zhang
Yuan Zhang
LHLujun Han
Lujun Han
LMLi Mao
Li Mao
ZLZaiyi Liu
Zaiyi Liu
Objective: This study aims to develop and externally validate a contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics-based model for preoperative differentiation between fat-poor angiomyolipoma (fp-AML) and hepatocellular carcinoma (HCC) in patients with noncirrhotic livers and to compare the diagnostic performance with that of two radiologists.Methods: This retrospective study was performed with 165 patients with noncirrhotic livers from three medical centers. The dataset was divided into a training cohort (n = 99), a time-independent internal validation cohort (n = 24) from one center, and an external validation cohort (n = 42) from the remaining two centers. The volumes of interest were contoured on the arterial phase (AP) images and then registered to the venous phase (VP) and delayed phase (DP), and a total of 3,396 radiomics features were extracted from the three phases. After the joint mutual information maximization feature selection procedure, four radiomics logistic regression classifiers, including the AP model, VP model, DP model, and combined model, were built. The area under the receiver operating characteristic curve (AUC), diagnostic accuracy, sensitivity, and specificity of each radiomics model and those of two radiologists were evaluated and compared.Results: The AUCs of the combined model reached 0.789 (95%CI, 0.579–0.999) in the internal validation cohort and 0.730 (95%CI, 0.563–0.896) in the external validation cohort, higher than the AP model (AUCs, 0.711 and 0.638) and significantly higher than the VP model (AUCs, 0.594 and 0.610) and the DP model (AUCs, 0.547 and 0.538). The diagnostic accuracy, sensitivity, and specificity of the combined model were 0.708, 0.625, and 0.750 in the internal validation cohort and 0.619, 0.786, and 0.536 in the external validation cohort, respectively. The AUCs for the two radiologists were 0.656 and 0.594 in the internal validation cohort and 0.643 and 0.500 in the external validation cohort. The AUCs of the combined model surpassed those of the two radiologists and were significantly higher than that of the junior one in both validation cohorts.Conclusions: The proposed radiomics model based on triple-phase CE-MRI images was proven to be useful for differentiating between fp-AML and HCC and yielded comparable or better performance than two radiologists in different centers, with different scanners and different scanning parameters.

2 February 2023 in Abdominal Radiology
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LJLi Junhao
Li Junhao
ZHZhang Hongxia
Zhang Hongxia
GJGuo Jiajun
Guo Jiajun
IAIkram Ahmad
Ikram Ahmad
GSGao Shanshan
Gao Shanshan
MWMingliang Wang
Mingliang Wang

24 May 2025 in European Radiology
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LSLeilei Shen
Leilei Shen
JLJinjuan Lu
Jinjuan Lu
CZChun Zhou
Chun Zhou
ZBZhenghong Bi
Zhenghong Bi
XYXiaodan Ye
Xiaodan Ye
MWMingliang Wang
Mingliang Wang

23 September 2024 in Abdominal Radiology
Verified
Journal Article
Closed access
PDF file
XLXiaohui Liu
Xiaohui Liu
XHXiaowei Han
Xiaowei Han
XWXu Wang
Xu Wang
KXKaiyuan Xu
Kaiyuan Xu
MWMingliang Wang
Mingliang Wang
GZGuozheng Zhang
Guozheng Zhang