Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Xiren Xu"'
Publikováno v:
Asian Journal of Surgery, Vol 47, Iss 5, Pp 2122-2131 (2024)
Rectal cancer (RC) is the third most frequently diagnosed cancer worldwide, and the status of its circumferential resection margin (CRM) is of paramount significance for treatment strategies and prognosis. CRM involvement is defined as tumor touching
Externí odkaz:
https://doaj.org/article/f0888981b91a4bcaa9217cff584a2727
Publikováno v:
BMC Gastroenterology, Vol 22, Iss 1, Pp 1-8 (2022)
Abstract Background To construct clinical and machine learning nomogram for predicting the lymph node metastasis (LNM) status of rectal carcinoma (RC) based on radiomics and clinical characteristics. Methods 788 RC patients were enrolled from January
Externí odkaz:
https://doaj.org/article/b9ebd19fde26477ea4423b463b1817d6
Publikováno v:
BMC Cancer, Vol 22, Iss 1, Pp 1-8 (2022)
Abstract Object To evaluate the difference between multiple primary lung adenocarcinoma (MPLA) and solitary primary lung adenocarcinoma (SPLA) by delta-radiomics based machine learning algorithms in CT images. Methods A total of 1094 patients contain
Externí odkaz:
https://doaj.org/article/6e4dc4078b124e4fa536270257037069
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
Abstract This study aimed to construct convention-radiomics CT nomogram containing conventional CT characteristics and radiomics signature for distinguishing fat-poor angiomyolipoma (fp-AML) from clear-cell renal cell carcinoma (ccRCC). 29 fp-AML and
Externí odkaz:
https://doaj.org/article/bce985b8f9ef4c4c99700096b5b80509
Publikováno v:
Frontiers in Oncology, Vol 10 (2020)
Purpose: This study aimed to explore the role of delta-radiomics in differentiating pre-invasive ground-glass nodules (GGNs) from invasive GGNs, compared with radiomics signature.Materials and Methods: A total of 464 patients including 107 pre-invasi
Externí odkaz:
https://doaj.org/article/507f926e0a6c4ab7adcb4c8b16e08d3b
Publikováno v:
Cancer Management and Research.
Hang Yuan,1 Yu Peng,1 Xiren Xu,2 Shiliang Tu,1 Yuguo Wei,3 Yanqing Ma2 1Department of Colorectal Surgery, Zhejiang Provincial Peopleâs Hospital (Affiliated Peopleâs Hospital of Hangzhou Medical College), Hangzhou, Peopleâs Republic of
The CT delta-radiomics based machine learning approach in evaluating multiple primary lung carcinoma
Object: To evaluate the difference between multiple primary lung carcinoma (MPLC) and solitary primary lung carcinoma (SPLC) by delta-radiomics based machine learning algorithms in CT images. Methods A cohort of 1094 patients containing 268 MPLCs and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ddd177b96a8d3d3e5c773a4c03fe1a57
https://doi.org/10.21203/rs.3.rs-1728327/v1
https://doi.org/10.21203/rs.3.rs-1728327/v1
Publikováno v:
In Energy Procedia 2012 14:1979-1985
Publikováno v:
Cancer Management and Research
Yanqing Ma,1 Xiren Xu,1 Peipei Pang,2 Yang Wen1 1Department of Radiology, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, 310000, People’s Republic of China; 2Department of Pharmaceuticals Diagnos
Publikováno v:
Abdominal radiology (New York). 45(8)
This study aimed to discriminate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC) by constructing radiomics-based logistic classifiers in comparison with conventional computed tomography (CT) analysis at three CT phases.