Liu Z - Search Results

1

Value of digital mammography in predicting lymphovascular invasion of breast cancer.

Liu Z, Li R, Liang K, Chen J, Chen X, Li X, Li R, Zhang X, Yi L, Long W. Liu Z, et al. BMC Cancer. 2020 Apr 3;20(1):274. doi: 10.1186/s12885-020-6712-z. BMC Cancer. 2020. PMID: 32245448 Free PMC article.

2

Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics.

Liu Z, Feng B, Li C, Chen Y, Chen Q, Li X, Guan J, Chen X, Cui E, Li R, Li Z, Long W. Liu Z, et al. J Magn Reson Imaging. 2019 Sep;50(3):847-857. doi: 10.1002/jmri.26688. Epub 2019 Feb 17. J Magn Reson Imaging. 2019. PMID: 30773770

3

Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features.

Cui EM, Lin F, Li Q, Li RG, Chen XM, Liu ZS, Long WS. Cui EM, et al. Acta Radiol. 2019 Nov;60(11):1543-1552. doi: 10.1177/0284185119830282. Epub 2019 Feb 24. Acta Radiol. 2019. PMID: 30799634 No abstract available.

4

Whole-Lesion Computed Tomography-Based Entropy Parameters for the Differentiation of Minimally Invasive and Invasive Adenocarcinomas Appearing as Pulmonary Subsolid Nodules.

Chen X, Feng B, Chen Y, Hao Y, Duan X, Cui E, Liu Z, Zhang C, Long W. Chen X, et al. Among authors: liu z. J Comput Assist Tomogr. 2019 Sep/Oct;43(5):817-824. doi: 10.1097/RCT.0000000000000889. J Comput Assist Tomogr. 2019. PMID: 31343995

5

Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks.

Ma X, Wang J, Zheng X, Liu Z, Long W, Zhang Y, Wei J, Lu Y. Ma X, et al. Among authors: liu z. Phys Med Biol. 2020 May 19;65(10):105006. doi: 10.1088/1361-6560/ab7e7f. Phys Med Biol. 2020. PMID: 32155611

6

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography.

Chen X, Feng B, Li C, Duan X, Chen Y, Li Z, Liu Z, Zhang C, Long W. Chen X, et al. Among authors: liu z. Oncol Rep. 2020 Apr;43(4):1256-1266. doi: 10.3892/or.2020.7497. Epub 2020 Feb 11. Oncol Rep. 2020. PMID: 32323834 Free PMC article.

7

Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

Feng B, Chen X, Chen Y, Lu S, Liu K, Li K, Liu Z, Hao Y, Li Z, Zhu Z, Yao N, Liang G, Zhang J, Long W, Liu X. Feng B, et al. Among authors: liu k, liu z, liu x. Eur Radiol. 2020 Dec;30(12):6497-6507. doi: 10.1007/s00330-020-07024-z. Epub 2020 Jun 27. Eur Radiol. 2020. PMID: 32594210

8

Comparison of the clinical and MRI features of patients with hepatic hemangioma, epithelioid hemangioendothelioma, or angiosarcoma.

Liu Z, Yi L, Chen J, Li R, Liang K, Chen X, Li R, Long W. Liu Z, et al. BMC Med Imaging. 2020 Jun 29;20(1):71. doi: 10.1186/s12880-020-00465-4. BMC Med Imaging. 2020. PMID: 32600273 Free PMC article.

9

A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules.

Chen X, Feng B, Chen Y, Liu K, Li K, Duan X, Hao Y, Cui E, Liu Z, Zhang C, Long W, Liu X. Chen X, et al. Among authors: liu k, liu z, liu x. Cancer Imaging. 2020 Jul 8;20(1):45. doi: 10.1186/s40644-020-00320-3. Cancer Imaging. 2020. PMID: 32641166 Free PMC article.

10

Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes.

Li X, Yu Q, Chen J, Huang H, Liu Z, Wang C, He Y, Zhang X, Li W, Li C, Zhao J, Long W. Li X, et al. Among authors: liu z. PeerJ. 2020 Nov 4;8:e10249. doi: 10.7717/peerj.10249. eCollection 2020. PeerJ. 2020. PMID: 33194424 Free PMC article.

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