A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma.

Yuan, Hang; Peng, Yu; Xu, Xiren; Tu, Shiliang; Wei, Yuguo; Ma, Yanqing

Yuan, Hang; Peng, Yu; Xu, Xiren; Tu, Shiliang; Wei, Yuguo; Ma, Yanqing.

Afiliação

Yuan H; Department of Colorectal Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, People's Republic of China.
Peng Y; Department of Colorectal Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, People's Republic of China.
Xu X; Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, People's Republic of China.
Tu S; Department of Colorectal Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, People's Republic of China.
Wei Y; GE Healthcare, Precision Health Institution, Hangzhou, People's Republic of China.
Ma Y; Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, People's Republic of China.

Cancer Manag Res ; 14: 2409-2418, 2022.

Article em En | MEDLINE | ID: mdl-35971393

Palavras-chave

computed tomography; machine learning; microsatellite instability; nomogram; radiomics; rectal carcinoma

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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