Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer.

Journal: BMC cancer
PMID: 39979837

Abstract

BACKGROUND: Bladder cancer (BLCA) exists a profound molecular diversity, with basal and luminal subtypes having different prognostic and therapeutic outcomes. Traditional methods for molecular subtyping are often time-consuming and resource-intensive. This study aims to develop machine learning models using deep learning features from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) to predict basal and luminal subtypes in BLCA.

Authors

  • Zongtai Zheng
    Department of Urology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, China.
  • Fazhong Dai
    Department of Urology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, China.
  • Ji Liu
  • Yongqiang Zhang
    School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China.
  • Zhenwei Wang
    Orthopedics Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China. fmmuwzw@163.com.
  • Bangqi Wang
    Department of Urology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, China. bangqi916@163.com.
  • Xiaofu Qiu
    Department of Urology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, China. qiuxf@gd2h.org.cn.

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