Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Hongna, Tan"'
Autor:
Jing Zhou, Xuan Yu, Qingxia Wu, Yaping Wu, Cong Fu, Yunxia Wang, Menglu Hai, Hongna Tan, Meiyun Wang
Publikováno v:
Heliyon, Vol 10, Iss 7, Pp e28722- (2024)
Purpose: To investigate the potential of radiomics signatures (RSs) from intratumoral and peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively evaluate HER2 status in breast cancer. Method: In this retrospective st
Externí odkaz:
https://doaj.org/article/97536f8b1ff2480f818158b83d646618
Autor:
Yunxia Wang, Yiyan Shang, Yaxin Guo, Menglu Hai, Yang Gao, Qingxia Wu, Shunian Li, Jun Liao, Xiaojuan Sun, Yaping Wu, Meiyun Wang, Hongna Tan
Publikováno v:
Frontiers in Oncology, Vol 14 (2024)
ObjectiveTo investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer.MethodsA total of 4
Externí odkaz:
https://doaj.org/article/b59645cb3025493e8f08db041d052708
Autor:
Shanshan Wang, Cheng Li, Rongpin Wang, Zaiyi Liu, Meiyun Wang, Hongna Tan, Yaping Wu, Xinfeng Liu, Hui Sun, Rui Yang, Xin Liu, Jie Chen, Huihui Zhou, Ismail Ben Ayed, Hairong Zheng
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, the authors introduce an open-source framework to handl
Externí odkaz:
https://doaj.org/article/89468834a479418fa700e90078bef195
Publikováno v:
PLoS ONE, Vol 16, Iss 9, p e0256995 (2021)
Acute myeloid leukemia (AML) is as a highly aggressive and heterogeneous hematological malignancy. MiR-20a-5p has been reported to function as an oncogene or tumor suppressor in several tumors, but the clinical significance and regulatory mechanisms
Externí odkaz:
https://doaj.org/article/0c417fb8567e4269b36a5b936c5ea5d7
Autor:
Huijuan Xiao, Yihe Liu, Pan Liang, Bo Wang, Hongna Tan, Yonggao Zhang, Xianzheng Gao, Jianbo Gao
Publikováno v:
Cell & Bioscience, Vol 8, Iss 1, Pp 1-13 (2018)
Abstract Background The acquisition of drug resistance has been considered as a main obstacle for cancer chemotherapy. Tumor protein 53 target gene 1 (TP53TG1), a p53-induced lncRNA, plays a vital role in the progression of human cancers. However, li
Externí odkaz:
https://doaj.org/article/a47e1d5918704424a9ccf1d5cdaf40af
Autor:
Hongna Tan, Qingxia Wu, Yaping Wu, Bingjie Zheng, Bo Wang, Yan Chen, Lijuan Du, Jing Zhou, Fangfang Fu, Huihui Guo, Cong Fu, Lun Ma, Pei Dong, Zhong Xue, Dinggang Shen, Meiyun Wang
Background: Recent artificial intelligence has exhibited great potential in breast imaging, but its value in precise risk stratification of mammography still needs further investigation. This study is to develop an artificial intelligence system (AIS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9db2f6e4d7aa32aa59245dbe4031cda
https://doi.org/10.21203/rs.3.rs-2489648/v1
https://doi.org/10.21203/rs.3.rs-2489648/v1
Autor:
Jie Chen, Huihui Zhou, Rui Yang, Hui Sun, Yaping Wu, Rongpin Wang, Zaiyi Liu, Xinfeng Liu, Ismail Ben Ayed, Xin Liu, Shanshan Wang, Hongna Tan, Hairong Zheng, Meiyun Wang, Cheng Li
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
Nature Communications
Nature Communications
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to
Autor:
Zehua Liu, Aozi Feng, Meiyun Wang, Jing Zhou, Yaping Wu, Wei Li, Huan Liu, Fangfang Fu, Hongna Tan, Yan Bai, Xin Jia
Publikováno v:
Academic Radiology. 28:1352-1360
Objectives The aim of our study was to preoperatively predict the human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer using radiomics signatures based on single-parametric and multiparametric magnetic resonance imagi
Autor:
Qingxia Wu, Hongna Tan, Yaping Wu, Pei Dong, Jifei Che, Zheren Li, Chenjin Lei, Dinggang Shen, Zhong Xue, Meiyun Wang
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783031210136
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9dcfd587f7d85f4c02db5cc37e37254
https://doi.org/10.1007/978-3-031-21014-3_14
https://doi.org/10.1007/978-3-031-21014-3_14
Publikováno v:
Journal of Healthcare Engineering, Vol 2019 (2019)
Journal of Healthcare Engineering
Journal of Healthcare Engineering
Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates. In this paper, we propose a fully convolut