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Machine Learning-Assisted Ensemble Analysis for the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer.
Huang, Yibao; Zhu, Qingqing; Xue, Liru; Zhu, Xiaoran; Chen, Yingying; Wu, Mingfu.
Afiliación
  • Huang Y; Department of Gynecology, National Clinical Research Center for Obstetrical and Gynecological Diseases, Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhu Q; Department of Gynecology, National Clinical Research Center for Obstetrical and Gynecological Diseases, Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xue L; Department of Gynecology, National Clinical Research Center for Obstetrical and Gynecological Diseases, Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhu X; Department of Gynecology, National Clinical Research Center for Obstetrical and Gynecological Diseases, Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Chen Y; Department of Gynecology, National Clinical Research Center for Obstetrical and Gynecological Diseases, Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Wu M; Department of Gynecology, National Clinical Research Center for Obstetrical and Gynecological Diseases, Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Oncol ; 12: 817250, 2022.
Article en En | MEDLINE | ID: mdl-35425697
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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: Front Oncol Año: 2022 Tipo del documento: Article País de afiliación: China
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Año: 2022 Tipo del documento: Article País de afiliación: China