Feng B - Search Results

1

Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model.

Lei Y, Feng B, Wan M, Xu K, Cui J, Ma C, Sun J, Yao C, Gan S, Shi J, Cui E. Lei Y, et al. Among authors: feng b. Abdom Radiol (NY). 2024 May;49(5):1397-1410. doi: 10.1007/s00261-024-04202-1. Epub 2024 Mar 3. Abdom Radiol (NY). 2024. PMID: 38433144

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. Among authors: feng b. 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

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: feng b. J Comput Assist Tomogr. 2019 Sep/Oct;43(5):817-824. doi: 10.1097/RCT.0000000000000889. J Comput Assist Tomogr. 2019. PMID: 31343995

4

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. Eur Radiol. 2020 Dec;30(12):6497-6507. doi: 10.1007/s00330-020-07024-z. Epub 2020 Jun 27. Eur Radiol. 2020. PMID: 32594210

5

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: feng b. Cancer Imaging. 2020 Jul 8;20(1):45. doi: 10.1186/s40644-020-00320-3. Cancer Imaging. 2020. PMID: 32641166 Free PMC article.

6

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

Chen X, Feng B, Chen Y, Duan X, Liu K, Li K, Zhang C, Liu X, Long W. Chen X, et al. Among authors: feng b. Eur J Radiol. 2021 Dec;145:110041. doi: 10.1016/j.ejrad.2021.110041. Epub 2021 Nov 15. Eur J Radiol. 2021. PMID: 34837794 Free article.

7

Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics.

Feng B, Liu Z, Liu Y, Chen Y, Zhou H, Cui E, Li X, Chen X, Li R, Yu T, Zhang L, Long W. Feng B, et al. Front Oncol. 2022 Sep 15;12:890659. doi: 10.3389/fonc.2022.890659. eCollection 2022. Front Oncol. 2022. PMID: 36185309 Free PMC article.

8

Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies.

Zhou T, Guan J, Feng B, Xue H, Cui J, Kuang Q, Chen Y, Xu K, Lin F, Cui E, Long W. Zhou T, et al. Among authors: feng b. Eur Radiol. 2023 Jun;33(6):4323-4332. doi: 10.1007/s00330-022-09384-0. Epub 2023 Jan 16. Eur Radiol. 2023. PMID: 36645455

9

Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning.

Feng B, Chen X, Chen Y, Yu T, Duan X, Liu K, Li K, Liu Z, Lin H, Li S, Chen X, Ke Y, Li Z, Cui E, Long W, Liu X. Feng B, et al. Cancers (Basel). 2023 Jan 31;15(3):892. doi: 10.3390/cancers15030892. Cancers (Basel). 2023. PMID: 36765850 Free PMC article.

10

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

Chen X, Feng B, Xu K, Chen Y, Duan X, Jin Z, Li K, Li R, Long W, Liu X. Chen X, et al. Among authors: feng b. Eur Radiol. 2023 Oct;33(10):6804-6816. doi: 10.1007/s00330-023-09690-1. Epub 2023 May 6. Eur Radiol. 2023. PMID: 37148352

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