Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Yingjie Xv"'
Autor:
Zongjie Wei, Xuesong Bai, Yingjie Xv, Shao-Hao Chen, Siwen Yin, Yang Li, Fajin Lv, Mingzhao Xiao, Yongpeng Xie
Publikováno v:
Insights into Imaging, Vol 15, Iss 1, Pp 1-11 (2024)
Abstract Objective To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to preoperatively predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer (BCa) with multicenter validation. M
Externí odkaz:
https://doaj.org/article/85431007c8e244a08305dd125c269f39
Autor:
Huayun Liu, Zongjie Wei, Yingjie Xv, Hao Tan, Fangtong Liao, Fajin Lv, Qing Jiang, Tao Chen, Mingzhao Xiao
Publikováno v:
Insights into Imaging, Vol 14, Iss 1, Pp 1-14 (2023)
Abstract Objective To develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC). Methods A total of 425 patients with localized ccRCC were enroll
Externí odkaz:
https://doaj.org/article/bdbff639aba5420bbac8d96fbd7466ec
Autor:
Zongjie Wei, Huayun Liu, Yingjie Xv, Fangtong Liao, Quanhao He, Yongpeng Xie, Fajin Lv, Qing Jiang, Mingzhao Xiao
Publikováno v:
Heliyon, Vol 10, Iss 2, Pp e24878- (2024)
Objective: This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods: In this retrospective
Externí odkaz:
https://doaj.org/article/559dc62a609a4e43a56eaaa91545dd53
Publikováno v:
Insights into Imaging, Vol 12, Iss 1, Pp 1-14 (2021)
Abstract Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 pati
Externí odkaz:
https://doaj.org/article/f4995fb21ba840a19dd8aef54220e3fe
Autor:
Yingjie Xv, Fajin Lv, Haoming Guo, Zhaojun Liu, Di Luo, Jing Liu, Xin Gou, Weiyang He, Mingzhao Xiao, Yineng Zheng
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
ObjectiveThis study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs).Methods370 patients with
Externí odkaz:
https://doaj.org/article/4e0313e519bd4cefbbcec6f4d3136cd8
Publikováno v:
Aging. 14:10093-10106
Bladder carcinoma (BC) represents one of the most prevalent malignant cancers, while predicting its clinical outcomes using traditional indicators is difficult. This study aimed to develop a miRNA signature for the prognostic prediction of patients w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec6187a7ddfb86611a2aa0aa8fe3ffee
https://doi.org/10.18632/aging.204447
https://doi.org/10.18632/aging.204447
Publikováno v:
Clinics, Volume: 77, Article number: 100056, Published: 08 AUG 2022
Objective: As a greater proportion of patients survived their initial cardiac insult, Chronic Heart Failure (CHF) is becoming a major cause of worldwide morbidity and mortality. However, the mechanism underlying the inflammation in patients with CHF
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2b93e81bd9591ed191f51f16a1b37f8
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-59322022000100235&lng=en&tlng=en
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-59322022000100235&lng=en&tlng=en
Publikováno v:
Insights into Imaging
Insights into Imaging, Vol 12, Iss 1, Pp 1-14 (2021)
Insights into Imaging, Vol 12, Iss 1, Pp 1-14 (2021)
Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::752c48ee153c633c107fe41d188c7d11
https://pubmed.ncbi.nlm.nih.gov/34800179
https://pubmed.ncbi.nlm.nih.gov/34800179