文献
J-GLOBAL ID:202202262782738149
整理番号:22A1025661
3.0T拡散尖度イメージングに基づくXGboost予測モデルはMRI BiRADS4腫瘤の診断精度を改善する【JST・京大機械翻訳】
XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses
著者 (10件):
Tang Wan
(1Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China)
,
Tang Wan
(2Institute of Health Monitoring, Inspection and Protection, Hubei Provincial Center for Disease Control and Prevention, Wuhan, China)
,
Zhou Han
(1Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China)
,
Quan Tianhong
(3Department of Electronic and information Engineering, College of Engineering, Shantou University, Shantou, China)
,
Chen Xiaoyan
(1Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China)
,
Zhang Huanian
(1Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China)
,
Lin Yan
(1Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China)
,
Lin Yan
(4Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, China)
,
Wu Renhua
(1Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China)
,
Wu Renhua
(4Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, China)
資料名:
Frontiers in Oncology (Web)
(Frontiers in Oncology (Web))
巻:
12
ページ:
833680
発行年:
2022年
JST資料番号:
U7089A
ISSN:
2234-943X
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
スイス (CHE)
言語:
英語 (EN)