The application of decision tree model based on clinicopathological risk factors and pre-operative MRI radiomics for predicting short-term recurrence of glioblastoma after total resection: a retrospective cohort study.

Du, Peng; Wu, Xuefan; Liu, Xiao; Chen, Jiawei; Chen, Lang; Cao, Aihong; Geng, Daoying

Du, Peng; Wu, Xuefan; Liu, Xiao; Chen, Jiawei; Chen, Lang; Cao, Aihong; Geng, Daoying.

Afiliación

Du P; Department of Radiology, Huashan Hospital, Fudan University Shanghai 200040, China.
Wu X; Department of Radiology, The Second Affiliated Hospital of Xuzhou Medical University Xuzhou 221000, Jiangsu, China.
Liu X; Department of Radiology, Shanghai Gamma Hospital Shanghai 200040, China.
Chen J; School of Computer and Information Technology, Beijing Jiaotong University Beijing 100044, China.
Chen L; Department of Neurosurgery, Huashan Hospital, Fudan University Shanghai 200040, China.
Cao A; Department of Radiology, The Second Affiliated Hospital of Xuzhou Medical University Xuzhou 221000, Jiangsu, China.
Geng D; Department of Radiology, The Second Affiliated Hospital of Xuzhou Medical University Xuzhou 221000, Jiangsu, China.

Am J Cancer Res ; 13(8): 3449-3462, 2023.

Article en En | MEDLINE | ID: mdl-37693142

Palabras clave

Decision tree model; MRI; deep-learning; glioblastoma; radiomics; short-term recurrence

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Health_economic_evaluation / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Am J Cancer Res Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

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