Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging.

Authors

  • Robin Wang
    Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Yeyu Cai
    Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China.
  • Iris K Lee
    Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
  • Rong Hu
    College of Chemistry and Chemical Engineering, Yunnan Normal University , Yunnan, Kunming, 650092, People's Republic of China.
  • Subhanik Purkayastha
    Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, Rhode Island.
  • Ian Pan
    Warren Alpert Medical School, Brown University, Providence, RI.
  • Thomas Yi
    Warren Alpert Medical School at Brown University, Providence, RI, USA.
  • Thi My Linh Tran
    From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pa (R.W.); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology. Massachusetts General Hospital, Boston, Mass (K.C.); Warren Alpert Medical School at Brown University, Providence, RI (H.X.B., K.H., T.M.L.T., J.W.C., I.P.); Department of Radiology, Yongzhou Central Hospital, Yongzhou, China (L.B.S.); Department of Radiology, Changde Second People's Hospital, Changde, China (J.M.); Department of Radiology, Affiliated Nan Hua Hospital, University of South China, Hengyang, China (X.L.J.); Department of Radiology, Loudi Central Hospital, Loudi, China (Q.H.Z.); Department of Radiology, Chenzhou Second People's Hospital, Chenzhou, China (P.F.H.); Department of Radiology, Zhuzhou Central Hospital, Zhuzhou, China (Y.H.L.); Department of Radiology, Yiyang City Center Hospital, Yiyang, China (F.X.F.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (R.Y.H.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (R.S.); and Department of Radiology, The First Hospital of Changsha, Changsha, China (Q.Z.Y.).
  • Shaolei Lu
    Department of Pathology, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, RI, USA.
  • Tao Liu
    Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Ken Chang
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Raymond Y Huang
    Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts. kalpathy@nmr.mgh.harvard.edu yangli762@gmail.com ryhuang@partners.org.
  • Paul J Zhang
    Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Zishu Zhang
    Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China. S.Stavropoulos@uphs.upenn.edu Harrison_Bai@Brown.edu zishuzhang@csu.edu.cn.
  • Enhua Xiao
    Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China.
  • Jing Wu
    School of Pharmaceutical Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
  • Harrison X Bai
    Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.