Deep learning radiomic nomogram outperforms the clinical model in distinguishing intracranial solitary fibrous tumors from angiomatous meningiomas and can predict patient prognosis.

Journal: European radiology
PMID:

Abstract

OBJECTIVES: To evaluate the value of a magnetic resonance imaging (MRI)-based deep learning radiomic nomogram (DLRN) for distinguishing intracranial solitary fibrous tumors (ISFTs) from angiomatous meningioma (AMs) and predicting overall survival (OS) for ISFT patients.

Authors

  • Xiaohong Liang
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Xiaoai Ke
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.
  • Wanjun Hu
    Department of Nuclear Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China.
  • Jian Jiang
    Eye Center of Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Shenglin Li
    College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
  • Caiqiang Xue
    Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China. Electronic address: 1102599617@qq.com.
  • Xianwang Liu
    Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China. Electronic address: 1553537867@qq.com.
  • Juan Dend
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.
  • Cheng Yan
    Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.
  • Mingzi Gao
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Liqin Zhao
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
  • Junlin Zhou
    Department of Radiology, Lanzhou University Second Hospital, 730030 Lanzhou, Gansu, China.