Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion.

Wan, Qi; Zhou, Jiaxuan; Xia, Xiaoying; Hu, Jianfeng; Wang, Peng; Peng, Yu; Zhang, Tianjing; Sun, Jianqing; Song, Yang; Yang, Guang; Li, Xinchun

Wan, Qi; Zhou, Jiaxuan; Xia, Xiaoying; Hu, Jianfeng; Wang, Peng; Peng, Yu; Zhang, Tianjing; Sun, Jianqing; Song, Yang; Yang, Guang; Li, Xinchun.

Affiliation

Wan Q; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Zhou J; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Xia X; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Hu J; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Wang P; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Peng Y; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Zhang T; Philips Healthcare, Guangzhou, China.
Sun J; Philips Healthcare, Guangzhou, China.
Song Y; Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China.
Yang G; Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China.
Li X; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.

Front Oncol ; 11: 683587, 2021.

Article in En | MEDLINE | ID: mdl-34868905

Key words

algorithms; area under the curve; lung neoplasms; machine learning; magnetic resonance imaging

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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies / Prognostic_studies Language: En Journal: Front Oncol Year: 2021 Type: Article Affiliation country: China

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