Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 40340752

Abstract

OBJECTIVE: Phyllodes tumors (PTs) are rare breast tumors with high recurrence rates, current methods relying on post-resection pathology often delay detection and require further surgery. We propose a deep-learning-based Phyllodes Tumors Hierarchical Diagnosis Model (PTs-HDM) for preoperative identification and grading.

Authors

  • Yuqi Yan
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Yuanzhen Liu
    Department of Diagnostic Ultrasound Imaging and Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310022, China; Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, Zhejiang 317502, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310018, China. Electronic address: yuanzhen_0128@aliyun.com.
  • Yao Wang
    Department of Gastrointestinal Surgery, Zhongshan People's Hospital, Zhongshan, Guangdong, China.
  • Tian Jiang
  • Jiayu Xie
    Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.
  • Yahan Zhou
    Wenling Medical Big Data and Artificial Intelligence Research Institute, Taizhou, China.
  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.
  • Meiying Yan
    Department of Ultrasound, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Cancer Hospital of University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, People's Republic of China.
  • Qiuqing Zheng
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, No.1 East Banshan Road, Gongshu District, Hangzhou, Zhejiang, 310022, China.
  • Haifei Xu
    School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.
  • Jinxiao Chen
    Department of Ultrasound, Lishui People's Hospital, Lishui, Zhejiang, China.
  • Lin Sui
    Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang 310022, China.
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Rongrong Ru
    Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, China.
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Anli Zhao
    Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China.
  • Shiyan Li
    Department of Ultrasound in Medicine, Affiliated Sir Run Run Shaw Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Ying Zhu
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Vicky Yang Wang
    Wenling Institute of Big Data and Artificial Intelligence in Medicine, Taizhou, 317502, China.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

Keywords

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