Machine Learning-Based Gray-Level Co-Occurrence Matrix (GLCM) Models for Predicting the Depth of Myometrial Invasion in Patients with Stage I Endometrial Cancer.

Qin, Li; Lai, Lin; Wang, Hongli; Zhang, Yukun; Qian, Xiaoyuan; He, Du

Qin, Li; Lai, Lin; Wang, Hongli; Zhang, Yukun; Qian, Xiaoyuan; He, Du.

Afiliación

Qin L; Department of Obstetrics and Gynecology, the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, 445000, People's Republic of China.
Lai L; Department of Oncology, the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, 445000, People's Republic of China.
Wang H; Department of Pathology, the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, 445000, People's Republic of China.
Zhang Y; Department of Oncology, the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, 445000, People's Republic of China.
Qian X; Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430079, People's Republic of China.
He D; Department of Oncology, the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, 445000, People's Republic of China.

Cancer Manag Res ; 14: 2143-2154, 2022.

Article en En | MEDLINE | ID: mdl-35795827

Palabras clave

endometrial cancer; gray level co-occurrence matrix; machine learning; myometrial invasion; prediction

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cancer Manag Res Año: 2022 Tipo del documento: Article Pais de publicación: Nueva Zelanda

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