A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma.

Journal: Computer methods and programs in biomedicine
PMID: 35104686

Abstract

BACKGROUND AND OBJECTIVE: Craniopharyngioma is a kind of benign brain tumor in histography. However, it might be clinically aggressive and have severe manifestations, such as increased intracranial pressure, hypothalamic-pituitary dysfunction, and visual impairment. It is considered challenging for radiologists to predict the invasiveness of craniopharyngioma through MRI images. Therefore, developing a non-invasive method that can predict the invasiveness and boundary of CP as a reference before surgery is of clinical value for making more appropriate and individualized treatment decisions and reducing the occurrence of inappropriate surgical plan choices.

Authors

  • Lin Zhu
    Institute of Environmental Technology, College of Environmental and Resource Sciences; Zhejiang University, Hangzhou 310058, China.
  • Lingling Zhang
    Department of Information Technology, Hunan Women's University, Changsha, Hunan 410002, PR China. Electronic address: linglingmath@gmail.com.
  • Wenxing Hu
  • Haixu Chen
    Institute of Geriatrics&National Clinical Research Center of Geriatrics Disease, The Second Medical Center, Chinese PLA General Hospital, Beijing, 100853, China.
  • Han Li
  • Shoushui Wei
    School of Control Science and Engineering, Shandong University, Jinan 250061, China.
  • Xuzhu Chen
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China. Electronic address: radiology888@aliyun.com.
  • Xibo Ma
    CBSR&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

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