A pilot study using a machine-learning approach of morphological and hemodynamic parameters for predicting aneurysms enhancement.

Journal: International journal of computer assisted radiology and surgery
Published Date: Aug 1, 2020

Abstract

PURPOSE: The development of straightforward classification methods is needed to identify unstable aneurysms and rupture risk for clinical use. In this study, we aim to investigate the relative importance of geometrical, hemodynamic and clinical risk factors represented by the PHASES score for predicting aneurysm wall enhancement using several machine-learning (ML) models.

Authors

  • Nan Lv
    Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai, China.
  • Christof Karmonik
    Translational Imaging Center, Houston Methodist Research Institute, Houston, TX, USA. ckarmonik@houstonmethodist.org.
  • Zhaoyue Shi
    Translational Imaging Center, Houston Methodist Research Institute, Houston, TX, USA.
  • Shiyue Chen
    Department of Radiology, Changhai Hospital, Second Military Medical University, Shanghai, China.
  • Xinrui Wang
    State Key Laboratory for Medical Genomics, Shanghai Institute of Hematology, Rui-Jin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China.
  • Jianmin Liu
    Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai, China.
  • Qinghai Huang
    Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai, China.

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