Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study.

Zhang, Yajiao; Wu, Chao; Du, Jinglong; Xiao, Zhibo; Lv, Furong; Liu, Yanbing

Zhang, Yajiao; Wu, Chao; Du, Jinglong; Xiao, Zhibo; Lv, Furong; Liu, Yanbing.

Afiliação

Zhang Y; College of Medical Informatics, Chongqing Medical University, No.1 Medical College Road, Chongqing, China.
Wu C; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Du J; College of Medical Informatics, Chongqing Medical University, No.1 Medical College Road, Chongqing, China.
Xiao Z; College of Medical Informatics, Chongqing Medical University, No.1 Medical College Road, Chongqing, China.
Lv F; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Liu Y; College of Medical Informatics, Chongqing Medical University, No.1 Medical College Road, Chongqing, China. x1220295030@163.com.

Abdom Radiol (NY) ; 49(1): 258-270, 2024 Jan.

Article em En | MEDLINE | ID: mdl-37987856

Assuntos

Aprendizado Profundo; Neoplasias do Colo do Útero; Humanos; Feminino; Neoplasias do Colo do Útero/diagnóstico por imagem; Nomogramas; Radiômica; Imageamento por Ressonância Magnética; Fatores de Risco; Estudos Retrospectivos

Palavras-chave

Cervical cancer; Deep learning; Nomogram; Radiomics; Risk stratification

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias do Colo do Útero / Aprendizado Profundo Limite: Female / Humans Idioma: En Revista: Abdom Radiol (NY) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

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