Prediction of molecular subtypes of breast cancer using BI-RADS features based on a "white box" machine learning approach in a multi-modal imaging setting.

Journal: European journal of radiology
Published Date: Mar 21, 2019

Abstract

PURPOSE: To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images.

Authors

  • Mingxiang Wu
    Department of Radiology, Shenzhen People's Hospital, No.1017 Dongmen North Road, Luohu District, Shenzhen, Guangdong, 518020, PR China.
  • Xiaoling Zhong
    Department of Radiology, Shenzhen People's Hospital, No.1017 Dongmen North Road, Luohu District, Shenzhen, Guangdong, 518020, PR China.
  • Quanzhou Peng
    Department of Pathology, Shenzhen People's Hospital, No.1017 Dongmen North Road, Luohu District, Shenzhen, Guangdong, 518020, PR China.
  • Mei Xu
    Department of Radiology, Shenzhen People's Hospital, No.1017 Dongmen North Road, Luohu District, Shenzhen, Guangdong, 518020, PR China.
  • Shelei Huang
    Department of Radiology, Shenzhen People's Hospital, No.1017 Dongmen North Road, Luohu District, Shenzhen, Guangdong, 518020, PR China.
  • Jialin Yuan
    Department of Radiology, Shenzhen People's Hospital, No.1017 Dongmen North Road, Luohu District, Shenzhen, Guangdong, 518020, PR China.
  • Jie Ma
    Respiratory Department, Beijing Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China.
  • Tao Tan
    Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.

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