He C - Search Results

1

Predicting the Invasiveness of Pulmonary Adenocarcinomas in Pure Ground-Glass Nodules Using the Nodule Diameter: A Systematic Review, Meta-Analysis, and Validation in an Independent Cohort.

Liu J, Yang X, Li Y, Xu H, He C, Zhou P, Qing H. Liu J, et al. Among authors: he c. Diagnostics (Basel). 2024 Jan 8;14(2):147. doi: 10.3390/diagnostics14020147. Diagnostics (Basel). 2024. PMID: 38248024 Free PMC article. Review.

2

Development and validation of qualitative and quantitative models to predict invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules based on low-dose computed tomography during lung cancer screening.

Liu J, Yang X, Li Y, Xu H, He C, Qing H, Ren J, Zhou P. Liu J, et al. Among authors: he c. Quant Imaging Med Surg. 2022 May;12(5):2917-2931. doi: 10.21037/qims-21-912. Quant Imaging Med Surg. 2022. PMID: 35502397 Free PMC article.

3

Accuracy of Pulmonary Nodule Volumetry at Different Exposure Parameters in Low-Dose Computed Tomography: A Phantom Study.

Liu J, Qing H, Luo H, He C, Wang C, Ren J, Zhou P. Liu J, et al. Among authors: he c. J Comput Assist Tomogr. 2019 Nov/Dec;43(6):926-930. doi: 10.1097/RCT.0000000000000908. J Comput Assist Tomogr. 2019. PMID: 31453975

4

Accuracy of Pulmonary Nodule Volumetry Using Noise-Optimized Virtual Monoenergetic Image and Nonlinear Blending Image Algorithms in Dual-Energy Computed Tomography: A Phantom Study.

He C, Liu J, Hu S, Qing H, Qiao L, Luo H, Chen X, Zhou P. He C, et al. J Comput Assist Tomogr. 2020 Nov/Dec;44(6):847-851. doi: 10.1097/RCT.0000000000001102. J Comput Assist Tomogr. 2020. PMID: 32976271

5

Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules.

Liu J, Xu H, Qing H, Li Y, Yang X, He C, Ren J, Zhou P. Liu J, et al. Among authors: he c. Front Oncol. 2021 Feb 2;10:634298. doi: 10.3389/fonc.2020.634298. eCollection 2020. Front Oncol. 2021. PMID: 33604303 Free PMC article.

6

Quantitative parameters of enhanced dual-energy computed tomography for differentiating lung cancers from benign lesions in solid pulmonary nodules.

He C, Liu J, Li Y, Lin L, Qing H, Guo L, Hu S, Zhou P. He C, et al. Front Oncol. 2022 Oct 6;12:1027985. doi: 10.3389/fonc.2022.1027985. eCollection 2022. Front Oncol. 2022. PMID: 36276069 Free PMC article.

7

Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma.

Li Y, Liu J, Yang X, Xu F, Wang L, He C, Lin L, Qing H, Ren J, Zhou P. Li Y, et al. Among authors: he c. Radiol Med. 2023 Feb;128(2):191-202. doi: 10.1007/s11547-023-01591-z. Epub 2023 Jan 13. Radiol Med. 2023. PMID: 36637740

8

An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening.

Li Y, Liu J, Yang X, Wang A, Zang C, Wang L, He C, Lin L, Qing H, Ren J, Zhou P. Li Y, et al. Among authors: he c. Eur Radiol. 2023 May;33(5):3072-3082. doi: 10.1007/s00330-023-09453-y. Epub 2023 Feb 15. Eur Radiol. 2023. PMID: 36790469

9

The solid component within part-solid nodules: 3-dimensional quantification, correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas, and comparisons with 2-dimentional measures and semantic features in low-dose computed tomography.

Liu J, Xie C, Li Y, Xu H, He C, Qing H, Zhou P. Liu J, et al. Among authors: he c. Cancer Imaging. 2023 Jun 22;23(1):65. doi: 10.1186/s40644-023-00577-4. Cancer Imaging. 2023. PMID: 37349824 Free PMC article.

10

Screening baseline characteristics of early lung cancer on low-dose computed tomography with computer-aided detection in a Chinese population.

Liu Y, Luo H, Qing H, Wang X, Ren J, Xu G, Hu S, He C, Zhou P. Liu Y, et al. Among authors: he c. Cancer Epidemiol. 2019 Oct;62:101567. doi: 10.1016/j.canep.2019.101567. Epub 2019 Jul 18. Cancer Epidemiol. 2019. PMID: 31326849

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