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Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.
Gao, Feng; Jiang, Liren; Guo, Tuanjie; Lin, Jun; Xu, Weiqing; Yuan, Lin; Han, Yaqin; Yang, Jiji; Pan, Qi; Chen, Enhui; Zhang, Ning; Chen, Siteng; Wang, Xiang.
Afiliación
  • Gao F; Pathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Jiang L; Pathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Guo T; Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Lin J; Pathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Xu W; Pathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yuan L; Pathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Han Y; Pathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yang J; Pathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Pan Q; Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Chen E; Department of Pathology, Dongtai People's Hospital, Dongtai, Jiangsu, China.
  • Zhang N; Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. zn12235@rjh.com.cn.
  • Chen S; Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. siteng@sjtu.edu.cn.
  • Wang X; Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. xiang.wang1@shgh.cn.
J Transl Med ; 22(1): 568, 2024 Jun 14.
Article en En | MEDLINE | ID: mdl-38877591
ABSTRACT

BACKGROUND:

Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions.

METHODS:

A total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts.

RESULTS:

The AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis.

CONCLUSIONS:

In this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Células Renales / Aprendizaje Profundo / Neoplasias Renales / Metástasis Linfática Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Transl Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Células Renales / Aprendizaje Profundo / Neoplasias Renales / Metástasis Linfática Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Transl Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido