Zobrazeno 1 - 10
of 323
pro vyhledávání: '"Zaiyi Liu"'
Autor:
Yuan Gao, Sofia Ventura-Diaz, Xin Wang, Muzhen He, Zeyan Xu, Arlene Weir, Hong-Yu Zhou, Tianyu Zhang, Frederieke H. van Duijnhoven, Luyi Han, Xiaomei Li, Anna D’Angelo, Valentina Longo, Zaiyi Liu, Jonas Teuwen, Marleen Kok, Regina Beets-Tan, Hugo M. Horlings, Tao Tan, Ritse Mann
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-17 (2024)
Abstract Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited
Externí odkaz:
https://doaj.org/article/c0d62ff4a3f64984a6d212ffdc266b26
Autor:
Feng Li, Yu Qi, Kuo Li, Zhenwei Shi, Guanchao Ye, Guangyao Wu, Mingliang Wang, Chunyang Zhang, Leonard Wee, Andre Dekker, Chu Han, Zaiyi Liu, Yongde Liao
Publikováno v:
Journal for ImmunoTherapy of Cancer, Vol 12, Iss 9 (2024)
Objectives Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy
Externí odkaz:
https://doaj.org/article/cf3f69a414aa48689ba9701e2f9aa827
Autor:
Yunlin Zheng, Bingjiang Qiu, Shunli Liu, Ruirui Song, Xianqi Yang, Lei Wu, Zhihong Chen, Abudouresuli Tuersun, Xiaotang Yang, Wei Wang, Zaiyi Liu
Publikováno v:
EClinicalMedicine, Vol 75, Iss , Pp 102805- (2024)
Summary: Background: Early prediction of lymph node status after neoadjuvant chemotherapy (NAC) facilitates promptly optimization of treatment strategies. This study aimed to develop and validate a deep learning network (DLN) using baseline computed
Externí odkaz:
https://doaj.org/article/ffd63f92c1a14abf9801d6f0cf6dba90
Publikováno v:
Genome Biology, Vol 25, Iss 1, Pp 1-23 (2024)
Abstract Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics d
Externí odkaz:
https://doaj.org/article/04ae3f2596c2422cbf26e615cbf4d399
Autor:
Lisha Yao, Suyun Li, Quan Tao, Yun Mao, Jie Dong, Cheng Lu, Chu Han, Bingjiang Qiu, Yanqi Huang, Xin Huang, Yanting Liang, Huan Lin, Yongmei Guo, Yingying Liang, Yizhou Chen, Jie Lin, Enyan Chen, Yanlian Jia, Zhihong Chen, Bochi Zheng, Tong Ling, Shunli Liu, Tong Tong, Wuteng Cao, Ruiping Zhang, Xin Chen, Zaiyi Liu
Publikováno v:
EBioMedicine, Vol 104, Iss , Pp 105183- (2024)
Summary: Background: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep lea
Externí odkaz:
https://doaj.org/article/c4d996dd92c84913a7a8e5361855d835
Autor:
Ming Cai, Ke Zhao, Lin Wu, Yanqi Huang, Minning Zhao, Qingru Hu, Qicong Chen, Su Yao, Zhenhui Li, Xinjuan Fan, Zaiyi Liu, Ting Gao, Xiuyuan Hao
Publikováno v:
Chinese Medical Journal, Vol 137, Iss 4, Pp 421-430 (2024)
Abstract. Background:. Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution
Externí odkaz:
https://doaj.org/article/aa76dbfac6b9497695e284ead2240591
Autor:
Xiaorui Han, Yuan Guo, Huifen Ye, Zhihong Chen, Qingru Hu, Xinhua Wei, Zaiyi Liu, Changhong Liang
Publikováno v:
Breast Cancer Research, Vol 26, Iss 1, Pp 1-14 (2024)
Abstract Backgrounds Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research wa
Externí odkaz:
https://doaj.org/article/43a597149e3745318715f0e875da6178
Publikováno v:
Cancer Medicine, Vol 12, Iss 23, Pp 21256-21269 (2023)
Abstract Background Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, th
Externí odkaz:
https://doaj.org/article/be40562f4fc4488388496eadda8a132d
Autor:
Xipeng Pan, Siyang Feng, Yumeng Wang, Jiale Chen, Huan Lin, Zimin Wang, Feihu Hou, Cheng Lu, Xin Chen, Zhenbing Liu, Zhenhui Li, Yanfen Cui, Zaiyi Liu
Publikováno v:
Heliyon, Vol 10, Iss 10, Pp e30779- (2024)
Background and objective: Spatial interaction between tumor-infiltrating lymphocytes (TILs) and tumor cells is valuable in predicting the effectiveness of immune response and prognosis amongst patients with lung adenocarcinoma (LUAD). Recent evidence
Externí odkaz:
https://doaj.org/article/7a78a433db1c48599c8670ff19546266
Autor:
Qicong Chen, Ming Cai, Xinjuan Fan, Wenbin Liu, Gang Fang, Su Yao, Yao Xu, Qian Li, Yingnan Zhao, Ke Zhao, Zaiyi Liu, Zhihua Chen
Publikováno v:
BMC Cancer, Vol 23, Iss 1, Pp 1-10 (2023)
Abstract Background and objective In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based o
Externí odkaz:
https://doaj.org/article/e70e85cae02b4806bff4bf556c80d7eb