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Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer.
Lou, Xiaoying; Zhou, Niyun; Feng, Lili; Li, Zhenhui; Fang, Yuqi; Fan, Xinjuan; Ling, Yihong; Liu, Hailing; Zou, Xuan; Wang, Jing; Huang, Junzhou; Yun, Jingping; Yao, Jianhua; Huang, Yan.
Afiliación
  • Lou X; Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Zhou N; Guangdong Institute of Gastroenterology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Feng L; Tencent AI Lab, Shenzhen, China.
  • Li Z; Guangdong Institute of Gastroenterology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Fang Y; Department of Radiation Oncology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Fan X; Department of Pathology, Yunnan Cancer Hospital, Kunming, China.
  • Ling Y; Tencent AI Lab, Shenzhen, China.
  • Liu H; Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
  • Zou X; Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Wang J; Guangdong Institute of Gastroenterology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Huang J; Department of Pathology, Cancer Center of Sun Yat-sen University, Guangzhou, China.
  • Yun J; Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Yao J; Guangdong Institute of Gastroenterology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Huang Y; Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Front Oncol ; 12: 807264, 2022.
Article en En | MEDLINE | ID: mdl-35756653
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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 Pais de publicación: Suiza
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 Pais de publicación: Suiza