Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers.

Journal: EBioMedicine
Published Date: Jul 19, 2023

Abstract

BACKGROUND: For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides.

Authors

  • Yini Huang
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Zhao Yao
    Department of Electronic Engineering, Fudan University, Shanghai, China.
  • Lingling Li
    College of Biological Science and Engineering, Fuzhou University, No. 2 Xue Yuan Road, University Town, Fuzhou, Fujian 350108, China.
  • Rushuang Mao
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Weijun Huang
  • Zhengming Hu
    Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.
  • Yixin Hu
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Yun Wang
    Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, People's Republic of China.
  • Ruohan Guo
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Xiaofeng Tang
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Liang Yang
  • Yuanyuan Wang
    Department of Biotechnology, College of Life Science and Technology, Jinan University Guangzhou, 510632, China.
  • Rongzhen Luo
    Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, 510060, Guangzhou, China.
  • Jinhua Yu
    Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. jhyu@fudan.edu.cn.
  • Jianhua Zhou
    Department of Ultrasound, Sun Yat-sen University Cancer centre, State Key Laboratory of Oncology in South China, Collaborative Innovation centre for Cancer Medicine, Guangzhou, China.

Keywords

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